%--------------------------------------
%
% EXAMPLES ENTRIES FOR REFERENCES -
% OURS START AT LINE 200
%
%--------------------------------------
@ARTICLE{kelly,
  author={Kelly, J. L.},
  journal={The Bell System Technical Journal}, 
  title={A new interpretation of information rate}, 
  year={1956},
  volume={35},
  number={4},
  pages={917-926},
  keywords={},
  doi={10.1002/j.1538-7305.1956.tb03809.x}}
%------------- BEGIN OF EXCHANGE RATE PROJECT REFERENCES -----------------
@article{von2024internationalization,
  title={Internationalization of the Chinese Renminbi: Progress and Outlook},
  author={von Beschwitz, Bastian},
  journal={FEDS Notes},
  number={2024-08},
  pages={30--3},
  year={2024}
}

@article{jeon2022news,
  title={News as sources of jumps in stock returns: Evidence from 21 million news articles for 9000 companies},
  author={Jeon, Yoontae and McCurdy, Thomas H and Zhao, Xiaofei},
  journal={Journal of Financial Economics},
  volume={145},
  number={2},
  pages={1--17},
  year={2022},
  publisher={Elsevier}
}

@article{lee2025large,
  title={Large Language Models in Finance (FinLLMs)},
  author={Lee, Jean and Stevens, Nicholas and Han, Soyeon Caren},
  journal={Neural Computing and Applications},
  pages={1--15},
  year={2025},
  publisher={Springer}
}

@article{nie2022time,
  title={A time series is worth 64 words: Long-term forecasting with transformers},
  author={Nie, Yuqi and Nguyen, Nam H and Sinthong, Phanwadee and Kalagnanam, Jayant},
  journal={arXiv preprint arXiv:2211.14730},
  year={2022}
}

@article{ansari2024chronos,
  title={Chronos: Learning the language of time series},
  author={Ansari, Abdul Fatir and Stella, Lorenzo and Turkmen, Caner and Zhang, Xiyuan and Mercado, Pedro and Shen, Huibin and Shchur, Oleksandr and Rangapuram, Syama Sundar and Arango, Sebastian Pineda and Kapoor, Shubham and others},
  journal={arXiv preprint arXiv:2403.07815},
  year={2024}
}

@article{raffel2020exploring,
  title={Exploring the limits of transfer learning with a unified text-to-text transformer},
  author={Raffel, Colin and Shazeer, Noam and Roberts, Adam and Lee, Katherine and Narang, Sharan and Matena, Michael and Zhou, Yanqi and Li, Wei and Liu, Peter J},
  journal={Journal of machine learning research},
  volume={21},
  number={140},
  pages={1--67},
  year={2020}
}

@article{herzen2022darts,
  title={Darts: User-friendly modern machine learning for time series},
  author={Herzen, Julien and L{\"a}ssig, Francesco and Piazzetta, Samuele Giuliano and Neuer, Thomas and Tafti, L{\'e}o and Raille, Guillaume and Van Pottelbergh, Tomas and Pasieka, Marek and Skrodzki, Andrzej and Huguenin, Nicolas and others},
  journal={Journal of Machine Learning Research},
  volume={23},
  number={124},
  pages={1--6},
  year={2022}
}

@article{gatev2006pairs,
  title={Pairs trading: Performance of a relative-value arbitrage rule},
  author={Gatev, Evan and Goetzmann, William N and Rouwenhorst, K Geert},
  journal={The review of financial studies},
  volume={19},
  number={3},
  pages={797--827},
  year={2006},
  publisher={Oxford University Press}
}

@inproceedings{ding2015deep,
  title={Deep learning for event-driven stock prediction.},
  author={Ding, Xiao and Zhang, Yue and Liu, Ting and Duan, Junwen},
  booktitle={Ijcai},
  volume={15},
  pages={2327--2333},
  year={2015}
}

@inproceedings{zhang2010trading,
  title={Trading strategies to exploit blog and news sentiment},
  author={Zhang, Wenbin and Skiena, Steven},
  booktitle={Proceedings of the international AAAI conference on web and social media},
  volume={4},
  pages={375--378},
  year={2010}
}

@article{tetlock2007giving,
  title={Giving content to investor sentiment: The role of media in the stock market},
  author={Tetlock, Paul C},
  journal={The Journal of finance},
  volume={62},
  number={3},
  pages={1139--1168},
  year={2007},
  publisher={Wiley Online Library}
}

@article{nassirtoussi2014text,
  title={Text mining for market prediction: A systematic review},
  author={Nassirtoussi, Arman Khadjeh and Aghabozorgi, Saeed and Wah, Teh Ying and Ngo, David Chek Ling},
  journal={Expert Systems with Applications},
  volume={41},
  number={16},
  pages={7653--7670},
  year={2014},
  publisher={Elsevier}
}

@article{loughran2011liability,
  title={When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks},
  author={Loughran, Tim and McDonald, Bill},
  journal={The Journal of finance},
  volume={66},
  number={1},
  pages={35--65},
  year={2011},
  publisher={Wiley Online Library}
}

@article{sezer2020financial,
  title={Financial time series forecasting with deep learning: A systematic literature review: 2005--2019},
  author={Sezer, Omer Berat and Gudelek, Mehmet Ugur and Ozbayoglu, Ahmet Murat},
  journal={Applied soft computing},
  volume={90},
  pages={106181},
  year={2020},
  publisher={Elsevier}
}

@article{fatouros2023transforming,
  title={Transforming sentiment analysis in the financial domain with ChatGPT},
  author={Fatouros, Georgios and Soldatos, John and Kouroumali, Kalliopi and Makridis, Georgios and Kyriazis, Dimosthenis},
  journal={Machine Learning with Applications},
  volume={14},
  pages={100508},
  year={2023},
  publisher={Elsevier}
}

@mastersthesis{dave2024predicting,
  title={Predicting Forex Pair Movements: Integrating Sentiment Analysis, Technical, and Fundamental Indicators using Machine Learning and Deep Learning Models},
  author={Dave, Yash},
  school={Unitec Institute of Technology, School of Computing, Electrical and Applied Technology},
  year={2024},
  address={New Zealand},
  month={September},
  note={Supervisors: Dr. Soheil Varastehpour, Dr. Masoud Shakiba}
}

@article{liu2023fingpt,
  title={Fingpt: Democratizing internet-scale data for financial large language models},
  author={Liu, Xiao-Yang and Wang, Guoxuan and Yang, Hongyang and Zha, Daochen},
  journal={arXiv preprint arXiv:2307.10485},
  year={2023}
}

@inproceedings{finbert2,
author = {Xu, Xuan and Wen, Fufang and Chu, Beilin and Fu, Zhibing and Lin, Qinhong and Liu, Jiaqi and Fei, Binjie and Li, Yu and Zhou, Linna and Yang, Zhongliang},
title = {FinBERT2: A Specialized Bidirectional Encoder for Bridging the Gap in Finance-Specific Deployment of Large Language Models},
year = {2025},
isbn = {9798400714542},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3711896.3737219},
doi = {10.1145/3711896.3737219},
abstract = {In natural language processing (NLP), the focus has shifted from encoder-only tiny language models like BERT to decoder-only large language models(LLMs) such as GPT-3. However, LLMs' practical application in the financial sector has revealed three limitations: (1) LLMs often perform worse than fine-tuned BERT on discriminative tasks despite costing much higher computational resources, such as market sentiment analysis in financial reports; (2) Application on generative tasks heavily relies on retrieval augmented generation (RAG) methods to provide current and specialized information, with general retrievers showing suboptimal performance on domain-specific retrieval tasks; (3) There are additional inadequacies in other feature-based scenarios, such as topic modeling. We introduce FinBERT2, a specialized bidirectional encoder pretrained on a high-quality, financial-specific corpus of 32b tokens. This represents the largest known Chinese financial pretraining corpus for models of this parameter size. As a better backbone, FinBERT2 can bridge the gap in the financial-specific deployment of LLMs through the following achievements: (1) Discriminative fine-tuned models (Fin-Labelers) outperform other (Fin)BERT variants by 0.4\%-3.3\% and leading LLMs by 9.7\%-12.3\% on average across five financial classification tasks. (2) Contrastive fine-tuned models (Fin-Retrievers) outperform both open-source (e.g., +6.8\% avg improvement over BGE-base-zh) and proprietary (e.g., +4.2\% avg improvement over OpenAI's text-embedding-3-large) embedders across five financial retrieval tasks; (3) Building on FinBERT2 variants, we construct the Fin-TopicModel, which enables superior clustering and topic representation for financial titles. Our work revisits financial BERT models through comparative analysis with contemporary LLMs and offers practical insights for effectively utilizing FinBERT in the LLMs era.},
booktitle = {Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2},
pages = {5117–5128},
numpages = {12},
keywords = {FinBERT, dense retriever, domain-specific LM, financial NLP, pretraining, topic modeling},
location = {Toronto ON, Canada},
series = {KDD '25}
}

@article{araci2019finbert,
  title={Finbert: Financial sentiment analysis with pre-trained language models},
  author={Araci, Dogu},
  journal={arXiv preprint arXiv:1908.10063},
  year={2019}
}

@misc{leme2023finbertptbr,
  author = {Leme, Lucas},
  title = {FinBERT-PT-BR: Financial BERT for Brazilian Portuguese},
  year = {2023},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/lucas-leme/FinBERT-PT-BR}},
  note = {Accessed: 2024}
}

@misc{radford2018improving,
  title={Improving language understanding by generative pre-training},
  author={Radford, Alec and Narasimhan, Karthik and Salimans, Tim and Sutskever, Ilya and others},
  year={2018},
  publisher={San Francisco, CA, USA}
}

@inproceedings{devlin2019bert,
  title={Bert: Pre-training of deep bidirectional transformers for language understanding},
  author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
  booktitle={Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers)},
  pages={4171--4186},
  year={2019}
}

@article{vaswani2017attention,
  title={Attention is all you need},
  author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@article{lim2021temporal,
  title={Temporal fusion transformers for interpretable multi-horizon time series forecasting},
  author={Lim, Bryan and Ar{\i}k, Sercan {\"O} and Loeff, Nicolas and Pfister, Tomas},
  journal={International journal of forecasting},
  volume={37},
  number={4},
  pages={1748--1764},
  year={2021},
  publisher={Elsevier}
}

@article{wan2019multivariate,
  title={Multivariate temporal convolutional network: A deep neural networks approach for multivariate time series forecasting},
  author={Wan, Renzhuo and Mei, Shuping and Wang, Jun and Liu, Min and Yang, Fan},
  journal={Electronics},
  volume={8},
  number={8},
  pages={876},
  year={2019},
  publisher={MDPI}
}

@article{bai2018empirical,
  title={An empirical evaluation of generic convolutional and recurrent networks for sequence modeling},
  author={Bai, Shaojie and Kolter, J Zico and Koltun, Vladlen},
  journal={arXiv preprint arXiv:1803.01271},
  year={2018}
}

@inproceedings{lea2017temporal,
  title={Temporal convolutional networks for action segmentation and detection},
  author={Lea, Colin and Flynn, Michael D and Vidal, Rene and Reiter, Austin and Hager, Gregory D},
  booktitle={proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={156--165},
  year={2017}
}

@article{fischer2018deep,
  title={Deep learning with long short-term memory networks for financial market predictions},
  author={Fischer, Thomas and Krauss, Christopher},
  journal={European journal of operational research},
  volume={270},
  number={2},
  pages={654--669},
  year={2018},
  publisher={Elsevier}
}

@article{hochreiter1997long,
  title={Long short-term memory},
  author={Hochreiter, Sepp and Schmidhuber, J{\"u}rgen},
  journal={Neural computation},
  volume={9},
  number={8},
  pages={1735--1780},
  year={1997},
  publisher={MIT press}
}

@article{bollerslev1986generalized,
  title={Generalized autoregressive conditional heteroskedasticity},
  author={Bollerslev, Tim},
  journal={Journal of econometrics},
  volume={31},
  number={3},
  pages={307--327},
  year={1986},
  publisher={Elsevier}
}

@book{box2015time,
  title={Time series analysis: forecasting and control},
  author={Box, George EP and Jenkins, Gwilym M and Reinsel, Gregory C and Ljung, Greta M},
  year={2015},
  publisher={John Wiley \& Sons}
}

@misc{bis2022,
    title={Triennial Central Bank Survey of foreign exchange and Over-the-counter (OTC) derivatives markets in 2022},
    year=2022,
    url={https://www.bis.org/statistics/rpfx22.htm}
}

@article{prates2015regime,
  title={O regime de c{\^a}mbio flutuante no Brasil: 1999-2012: especificidades e dilemas},
  author={Prates, Daniela Magalh{\~a}es},
  year={2015},
  publisher={Instituto de Pesquisa Econ{\^o}mica Aplicada (Ipea)}
}

@article{meese1983empirical,
  title={Empirical exchange rate models of the seventies: Do they fit out of sample?},
  author={Meese, Richard A and Rogoff, Kenneth},
  journal={Journal of international economics},
  volume={14},
  number={1-2},
  pages={3--24},
  year={1983},
  publisher={Elsevier}
}

@article{rossi2013exchange,
  title={Exchange rate predictability},
  author={Rossi, Barbara},
  journal={Journal of economic literature},
  volume={51},
  number={4},
  pages={1063--1119},
  year={2013},
  publisher={American Economic Association 2014 Broadway, Suite 305, Nashville, TN 37203}
}

@article{blaskowitz2011economic,
  title={On economic evaluation of directional forecasts},
  author={Blaskowitz, Oliver and Herwartz, Helmut},
  journal={International journal of forecasting},
  volume={27},
  number={4},
  pages={1058--1065},
  year={2011},
  publisher={Elsevier}
}

@article{cheung2005empirical,
  title={Empirical exchange rate models of the nineties: Are any fit to survive?},
  author={Cheung, Yin-Wong and Chinn, Menzie D and Pascual, Antonio Garcia},
  journal={Journal of international money and finance},
  volume={24},
  number={7},
  pages={1150--1175},
  year={2005},
  publisher={Elsevier}
}

@article{moosa2014error,
  title={Error correction modelling and dynamic specifications as a conduit to outperforming the random walk in exchange rate forecasting},
  author={Moosa, Imad and Burns, Kelly},
  journal={Applied Economics},
  volume={46},
  number={25},
  pages={3107--3118},
  year={2014},
  publisher={Taylor \& Francis}
}

@article{costantini2016forecasting,
  title={Forecasting errors, directional accuracy and profitability of currency trading: The case of EUR/USD exchange rate},
  author={Costantini, Mauro and Cuaresma, Jesus Crespo and Hlouskova, Jaroslava},
  journal={Journal of Forecasting},
  volume={35},
  number={7},
  pages={652--668},
  year={2016},
  publisher={Wiley Online Library}
}

@article{crespo2018exchange,
  title={Exchange rate forecasting and the performance of currency portfolios},
  author={Crespo Cuaresma, Jesus and Fortin, Ines and Hlouskova, Jaroslava},
  journal={Journal of Forecasting},
  volume={37},
  number={5},
  pages={519--540},
  year={2018},
  publisher={Wiley Online Library}
}

@article{cheung2019exchange,
  title={Exchange rate prediction redux: New models, new data, new currencies},
  author={Cheung, Yin-Wong and Chinn, Menzie D and Pascual, Antonio Garcia and Zhang, Yi},
  journal={Journal of International Money and Finance},
  volume={95},
  pages={332--362},
  year={2019},
  publisher={Elsevier}
}

@misc{bomdiamercado,
  author       = {{Bom Dia Mercado}},
  title        = {Bom Dia Mercado - Informações de Mercado e Economia},
  howpublished = {\url{https://www.bomdiamercado.com.br/}},
  note         = {Accessed: 2024-01-03}
}




% ------------ END OF EXCHANGE RATE PROJECT REFERENCES --------------------

@article{akaike1974new,
  title={A new look at the statistical model identification},
  author={Akaike, Hirotugu},
  DOI={10.1007/978-1-4612-1694-0_16},
  journal={IEEE transactions on automatic control},
  volume={19},
  number={6},
  pages={716--723},
  year={1974},
  publisher={Ieee}
}

% --- Seminal paper ARIMA ----
@article{box1970time,
  title={Time series analysis: forecasting and control Holden-day San Francisco},
  author={Box, GEORGE EP and Jenkins, Gwilym M and Reinsel, G},
  DOI={DOI: 10.4236/cs.2016.710283},
  journal={BoxTime Series Analysis: Forecasting and Control Holden Day1970},
  year={1970}
}

@article{b3site,
  author={B3},
  journal={Retrieved from b3. com. br: https://www.b3.com.br},
  year = {2023},
}

@article{finance2024yahoo,
  title = {20 largest stock exchanges in the world 2023}, 
  author={Yahoo},
  journal={Retrieved from finance. yahoo. com: https://finance.yahoo.com/news/20-largest-stock-exchanges-world-175549152.html},
  year = {2024},
}



@article{Robotrader,
  author={BLK},
  journal={Retrieved from blk. com. br: https://www.blk.com.br/},
  year = {2023},
}


@article{sharpe1966mutual,
  title={Mutual fund performance},
  author={Sharpe, William F},
  DOI={10.1086/294846},
  journal={The Journal of business},
  volume={39},
  number={1},
  pages={119--138},
  year={1966},
  publisher={JSTOR}
}

@book{vidyamurthy2004pairs,
  author={Vidyamurthy, Ganapathy},
  title={Pairs Trading: quantitative methods and analysis},
  volume={217},
  year={2004},
  publisher={John Wiley \& Sons}
}

@article{Worldbank,
  author={WorldBank},
  title = {GDP Ranking 2023}, 
  journal={Retrieved from worldbank. org: https://datacatalog.worldbank.org/search/dataset/0038130/GDP-ranking},
  year = {2024},
}

% --- seminal paper of NBEATS -----
@article{oreshkin2019n,
  title={N-BEATS: Neural basis expansion analysis for interpretable time series forecasting},
  author={Oreshkin, Boris N and Carpov, Dmitri and Chapados, Nicolas and Bengio, Yoshua},
  DOI={10.48550/arXiv.1905.10437},
  journal={arXiv preprint arXiv:1905.10437},
  year={2019}
}

@inproceedings{challu2023nhits,
  title={Nhits: Neural hierarchical interpolation for time series forecasting},
  author={Challu, Cristian and Olivares, Kin G and Oreshkin, Boris N and Ramirez, Federico Garza and Canseco, Max Mergenthaler and Dubrawski, Artur},
  DOI={10.1609/aaai.v37i6.25854},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={37},
  pages={6989--6997},
  year={2023}
}

@article{liu2017intraday,
  title={Intraday pairs trading strategies on high frequency data: The case of oil companies},
  author={Liu, Bo and Chang, Lo-Bin and Geman, H{\'e}lyette},
  DOI={10.1080/14697688.2016.1184304},
  journal={Quantitative Finance},
  volume={17},
  number={1},
  pages={87--100},
  year={2017},
  publisher={Taylor \& Francis}
}

% --- Theory of pairs trading ------
@book{Bookstaber2007,
      title = {A Demon of Our Own Design: Markets, Hedge Funds, and the Perils of Financial Innovation},
	publisher = {John Wiley & Sons},
	author = {Bookstaber, R. M.},
        DOI={DOI: 10.1017/S1474747207003460},
	year = {2007}
}

% ----- matrix confusion -------
@article{fawcett2006introduction,
  title={An introduction to ROC analysis},
  author={Fawcett, Tom},
  DOI={10.1016/j.patrec.2005.10.010},
  journal={Pattern recognition letters},
  volume={27},
  number={8},
  pages={861--874},
  year={2006},
  publisher={Elsevier}
}


% -------- Sharpe Index --------------------
@article{sharpe1975adjusting,
  title={Adjusting for risk in portfolio performance measurement},
  author={Sharpe, William F},
  DOI={10.3905/jpm.1975.408513},
  journal={The Journal of Portfolio Management},
  volume={1},
  number={2},
  pages={29--34},
  year={1975},
  publisher={Institutional Investor Journals Umbrella}
}

@article{lhabitant2015high,
  title={High-frequency trading: past, present, and future},
  author={Lhabitant, Francois-Serge and Gregoriou, Greg N},
  DOI={10.1353/pfs.2002.0014},
  journal={Handbook of high frequency trading},
  pages={155--166},
  year={2015},
  publisher={Academic Press London}
}

@article{zaharudin2022high,
  title={High-frequency trading: Definition, implications, and controversies},
  author={Zaharudin, Khairul Zharif and Young, Martin R and Hsu, Wei-Huei},
  DOI={10.1111/joes.12434},
  journal={Journal of Economic Surveys},
  volume={36},
  number={1},
  pages={75--107},
  year={2022},
  publisher={Wiley Online Library}
}

@article{virgilio2017high,
  title={Is high-frequency trading tiering the financial markets?},
  author={Virgilio, Gianluca},
  DOI={10.1016/j.ribaf.2017.04.031},
  journal={Research in International Business and Finance},
  volume={41},
  pages={158--171},
  year={2017},
  publisher={Elsevier}
}

@article{bogomolov2013pairs,
  title={Pairs trading based on statistical variability of the spread process},
  author={Bogomolov, Timofei},
  DOI={10.1080/14697688.2012.748934},
  journal={Quantitative Finance},
  volume={13},
  number={9},
  pages={1411--1430},
  year={2013},
  publisher={Taylor \& Francis}
}

@article{krauss2017statistical,
  title={Statistical arbitrage pairs trading strategies: Review and outlook},
  author={Krauss, Christopher},
  DOI={doi.org/10.1111/joes.12153},
  journal={Journal of Economic Surveys},
  volume={31},
  number={2},
  pages={513--545},
  year={2017},
  publisher={Wiley Online Library}
}

@article{tokat2022pairs,
  title={Pairs trading: is it applicable to exchange-traded funds?},
  author={Tokat, Ekin and Hayrullaho{\u{g}}lu, Ahmet Cevdet},
  DOI={10.1016/j.bir.2021.08.001},
  journal={Borsa Istanbul Review},
  volume={22},
  number={4},
  pages={743--751},
  year={2022},
  publisher={Elsevier}
}


@article{dickey1979distribution,
  title={Distribution of the estimators for autoregressive time series with a unit root},
  author={Dickey, David A and Fuller, Wayne A},
  DOI={10.1080/01621459.1979.10482531},
  journal={Journal of the American statistical association},
  volume={74},
  number={366a},
  pages={427--431},
  year={1979},
  publisher={Taylor \& Francis}
}


@article{schwarz1978estimating,
  title={Estimating the dimension of a model},
  author={Schwarz, Gideon},
  DOI={10.1214/aos/1176344136},
  journal={The annals of statistics},
  pages={461--464},
  year={1978},
  publisher={JSTOR}
}

@article{hochreiter1997lstm,
author = {Hochreiter, Sepp and Schmidhuber, J\"{u}rgen},
title = {Long Short-Term Memory},
year = {1997},
issue_date = {November 15, 1997},
publisher = {MIT Press},
address = {Cambridge, MA, USA},
volume = {9},
number = {8},
issn = {0899-7667},
url = {https://doi.org/10.1162/neco.1997.9.8.1735},
doi = {10.1162/neco.1997.9.8.1735},
abstract = {Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.},
journal = {Neural Comput.},
month = {nov},
pages = {1735–1780},
numpages = {46}
}

@article{SAGHEER2019203,
title = {Time series forecasting of petroleum production using deep LSTM recurrent networks},
journal = {Neurocomputing},
volume = {323},
pages = {203-213},
year = {2019},
issn = {0925-2312},
doi = {https://doi.org/10.1016/j.neucom.2018.09.082},
url = {https://www.sciencedirect.com/science/article/pii/S0925231218311639},
author = {Alaa Sagheer and Mostafa Kotb},
keywords = {Time series forecasting, Deep neural networks, Recurrent neural networks, Long-short term memory, Petroleum production forecasting},
abstract = {Time series forecasting (TSF) is the task of predicting future values of a given sequence using historical data. Recently, this task has attracted the attention of researchers in the area of machine learning to address the limitations of traditional forecasting methods, which are time-consuming and full of complexity. With the increasing availability of extensive amounts of historical data along with the need of performing accurate production forecasting, particularly a powerful forecasting technique infers the stochastic dependency between past and future values is highly needed. In this paper, we propose a deep learning approach capable to address the limitations of traditional forecasting approaches and show accurate predictions. The proposed approach is a deep long-short term memory (DLSTM) architecture, as an extension of the traditional recurrent neural network. Genetic algorithm is applied in order to optimally configure DLSTM’s optimum architecture. For evaluation purpose, two case studies from the petroleum industry domain are carried out using the production data of two actual oilfields. Toward a fair evaluation, the performance of the proposed approach is compared with several standard methods, either statistical or soft computing. Using different measurement criteria, the empirical results show that the proposed DLSTM model outperforms other standard approaches.}
}

@article{Gers2000LearningTF,
  title={Learning to Forget: Continual Prediction with LSTM},
  author={Felix Alexander Gers and J{\"u}rgen Schmidhuber and Fred Cummins},
  journal={Neural Computation},
  year={2000},
  volume={12},
  pages={2451-2471},
  url={https://api.semanticscholar.org/CorpusID:11598600}
}

@article{abcd123,
author = {Elman, Jeffrey L.},
title = {Finding Structure in Time},
journal = {Cognitive Science},
volume = {14},
number = {2},
pages = {179-211},
doi = {https://doi.org/10.1207/s15516709cog1402\_1},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1207/s15516709cog1402_1},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1207/s15516709cog1402_1},
abstract = {Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory. In this approach, hidden unit patterns are fed back to themselves: the internal representations which develop thus reflect task demands in the context of prior internal states. A set of simulations is reported which range from relatively simple problems (temporal version of XOR) to discovering syntactic/semantic features for words. The networks are able to learn interesting internal representations which incorporate task demands with memory demands: indeed, in this approach the notion of memory is inextricably bound up with task processing. These representations reveal a rich structure, which allows them to be highly context-dependent, while also expressing generalizations across classes of items. These representations suggest a method for representing lexical categories and the type/token distinction.},
year = {1990}
}

@inproceedings{Malhotra2015LongST,
  title={Long Short Term Memory Networks for Anomaly Detection in Time Series},
  author={Pankaj Malhotra and Lovekesh Vig and Gautam M. Shroff and Puneet Agarwal},
  booktitle={The European Symposium on Artificial Neural Networks},
  year={2015},
  url={https://api.semanticscholar.org/CorpusID:43680425}
}

@ARTICLE{8141873,
  author={Karim, Fazle and Majumdar, Somshubra and Darabi, Houshang and Chen, Shun},
  journal={IEEE Access}, 
  title={LSTM Fully Convolutional Networks for Time Series Classification}, 
  year={2018},
  volume={6},
  number={},
  pages={1662-1669},
  keywords={Time series analysis;Recurrent neural networks;Feature extraction;Convolution;Computer architecture;Machine learning;Machine learning algorithms;Convolutional neural network;long short term memory recurrent neural network;time series classification},
  doi={10.1109/ACCESS.2017.2779939}}

@INPROCEEDINGS{7364089,
  author={Chen, Kai and Zhou, Yi and Dai, Fangyan},
  booktitle={2015 IEEE International Conference on Big Data (Big Data)}, 
  title={A LSTM-based method for stock returns prediction: A case study of China stock market}, 
  year={2015},
  volume={},
  number={},
  pages={2823-2824},
  keywords={Indexes;Stock markets;Training;Recurrent neural networks;Computational modeling;Predictive models;Computer architecture},
  doi={10.1109/BigData.2015.7364089}}

@INPROCEEDINGS{8332636,
  author={Yunpeng, Liu and Di, Hou and Junpeng, Bao and Yong, Qi},
  booktitle={2017 14th Web Information Systems and Applications Conference (WISA)}, 
  title={Multi-step Ahead Time Series Forecasting for Different Data Patterns Based on LSTM Recurrent Neural Network}, 
  year={2017},
  volume={},
  number={},
  pages={305-310},
  keywords={Time series analysis;Data models;Predictive models;Hidden Markov models;Training;Forecasting;Recurrent neural networks;time series;LSTM;multi-step ahead},
  doi={10.1109/WISA.2017.25}}

@INPROCEEDINGS{8614252,
  author={Siami-Namini, Sima and Tavakoli, Neda and Siami Namin, Akbar},
  booktitle={2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)}, 
  title={A Comparison of ARIMA and LSTM in Forecasting Time Series}, 
  year={2018},
  volume={},
  number={},
  pages={1394-1401},
  keywords={Time series analysis;Forecasting;Predictive models;Autoregressive processes;Economics;Deep learning;Data models;Deep Learning, Long Short-Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), Forecasting, Time Series Data},
  doi={10.1109/ICMLA.2018.00227}}

@ARTICLE{650093,
  author={Schuster, M. and Paliwal, K.K.},
  journal={IEEE Transactions on Signal Processing}, 
  title={Bidirectional recurrent neural networks}, 
  year={1997},
  volume={45},
  number={11},
  pages={2673-2681},
  keywords={Recurrent neural networks;Artificial neural networks;Training data;Databases;Probability;Shape;Parameter estimation;Speech recognition;Control systems;Telecommunication control},
  doi={10.1109/78.650093}}

@article{GRAVES2005602,
title = {Framewise phoneme classification with bidirectional LSTM and other neural network architectures},
journal = {Neural Networks},
volume = {18},
number = {5},
pages = {602-610},
year = {2005},
note = {IJCNN 2005},
issn = {0893-6080},
doi = {https://doi.org/10.1016/j.neunet.2005.06.042},
url = {https://www.sciencedirect.com/science/article/pii/S0893608005001206},
author = {Alex Graves and Jürgen Schmidhuber},
abstract = {In this paper, we present bidirectional Long Short Term Memory (LSTM) networks, and a modified, full gradient version of the LSTM learning algorithm. We evaluate Bidirectional LSTM (BLSTM) and several other network architectures on the benchmark task of framewise phoneme classification, using the TIMIT database. Our main findings are that bidirectional networks outperform unidirectional ones, and Long Short Term Memory (LSTM) is much faster and also more accurate than both standard Recurrent Neural Nets (RNNs) and time-windowed Multilayer Perceptrons (MLPs). Our results support the view that contextual information is crucial to speech processing, and suggest that BLSTM is an effective architecture with which to exploit it.11An abbreviated version of some portions of this article appeared in (Graves and Schmidhuber, 2005), as part of the IJCNN 2005 conference proceedings, published under the IEEE copyright.}
}

@INPROCEEDINGS{9005997,
  author={Siami-Namini, Sima and Tavakoli, Neda and Namin, Akbar Siami},
  booktitle={2019 IEEE International Conference on Big Data (Big Data)}, 
  title={The Performance of LSTM and BiLSTM in Forecasting Time Series}, 
  year={2019},
  volume={},
  number={},
  pages={3285-3292},
  keywords={Biological system modeling;Training;Data models;Logic gates;Time series analysis;Predictive models;Recurrent neural networks},
  doi={10.1109/BigData47090.2019.9005997}}

@article{DASILVA20233315,
title = {Comparing Long Short-Term Memory (LSTM) and bidirectional LSTM deep neural networks for power consumption prediction},
journal = {Energy Reports},
volume = {10},
pages = {3315-3334},
year = {2023},
issn = {2352-4847},
doi = {https://doi.org/10.1016/j.egyr.2023.09.175},
url = {https://www.sciencedirect.com/science/article/pii/S2352484723014208},
author = {Davi Guimarães {da Silva} and Anderson Alvarenga de Moura Meneses},
keywords = {Electric consumption forecast, Deep learning, Univariate time series, Deep neural networks, Long-Short Term Memory},
abstract = {Electric consumption prediction methods are investigated for many reasons, such as decision-making related to energy efficiency as well as for anticipating demand and the dynamics of the energy market. The objective of the present work is to compare two Deep Learning models, namely the Long Short-Term Memory (LSTM) model, and the Bi-directional LSTM (BLSTM) for univariate electric consumption Time Series (TS) short-term forecast model. The Data Sets (DSs) were selected for their different contexts and scales, with the goal of assessing the robustness of the models. Four DSs were used, related to the power consumption of: (a) a household in France; (b) a university building in Santarém, Brazil; (c) the Tétouan city zones, in Morocco; and (d) the aggregated electric demand of Singapore. The metrics RMSE, MAE, MAPE and R2 were calculated in a TS cross-validation scheme. Friedman’s test was applied to normalized RMSE (NRMSE) results, showing that BLSTM outperforms LSTM with statistically significant difference (p = 0.0455), corroborating the fact that bidirectional weight updating significantly improves the LSTM performance with respect to different scales of electric power consumption. The present work provides statistical evidence supporting the conclusion that BLSTM outperforms LSTM models according to the tests performed, based on a complete methodology for TS prediction, and also establishes a baseline for future investigation of electric consumption TS prediction.}
}

@misc{wen2023transformers,
      title={Transformers in Time Series: A Survey}, 
      author={Qingsong Wen and Tian Zhou and Chaoli Zhang and Weiqi Chen and Ziqing Ma and Junchi Yan and Liang Sun},
      year={2023},
      eprint={2202.07125},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@inproceedings{wolf-etal-2020-transformers,
    title = "Transformers: State-of-the-Art Natural Language Processing",
    author = "Wolf, Thomas  and
      Debut, Lysandre  and
      Sanh, Victor  and
      Chaumond, Julien  and
      Delangue, Clement  and
      Moi, Anthony  and
      Cistac, Pierric  and
      Rault, Tim  and
      Louf, Remi  and
      Funtowicz, Morgan  and
      Davison, Joe  and
      Shleifer, Sam  and
      von Platen, Patrick  and
      Ma, Clara  and
      Jernite, Yacine  and
      Plu, Julien  and
      Xu, Canwen  and
      Le Scao, Teven  and
      Gugger, Sylvain  and
      Drame, Mariama  and
      Lhoest, Quentin  and
      Rush, Alexander",
    editor = "Liu, Qun  and
      Schlangen, David",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.emnlp-demos.6",
    doi = "10.18653/v1/2020.emnlp-demos.6",
    pages = "38--45",
    abstract = "Recent progress in natural language processing has been driven by advances in both model architecture and model pretraining. Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to effectively utilize this capacity for a wide variety of tasks. Transformers is an open-source library with the goal of opening up these advances to the wider machine learning community. The library consists of carefully engineered state-of-the art Transformer architectures under a unified API. Backing this library is a curated collection of pretrained models made by and available for the community. Transformers is designed to be extensible by researchers, simple for practitioners, and fast and robust in industrial deployments. The library is available at \url{https://github.com/huggingface/transformers}.",
}

@article{CHOI2018171,
title = {Fine-grained attention mechanism for neural machine translation},
journal = {Neurocomputing},
volume = {284},
pages = {171-176},
year = {2018},
issn = {0925-2312},
doi = {https://doi.org/10.1016/j.neucom.2018.01.007},
url = {https://www.sciencedirect.com/science/article/pii/S0925231218300225},
author = {Heeyoul Choi and Kyunghyun Cho and Yoshua Bengio},
keywords = {Neural machine translation, Attention mechanism, Fine-grained attention},
abstract = {Neural machine translation (NMT) has been a new paradigm in machine translation, and the attention mechanism has become the dominant approach with the state-of-the-art records in many language pairs. While there are variants of the attention mechanism, all of them use only temporal attention where one scalar value is assigned to one context vector corresponding to a source word. In this paper, we propose a fine-grained (or 2D) attention mechanism where each dimension of a context vector will receive a separate attention score. In experiments with the task of En-De and En-Fi translation, the fine-grained attention method improves the translation quality in terms of BLEU score. In addition, our alignment analysis reveals how the fine-grained attention mechanism exploits the internal structure of context vectors.}
}

@misc{cholakov2021transformers,
      title={Transformers predicting the future. Applying attention in next-frame and time series forecasting}, 
      author={Radostin Cholakov and Todor Kolev},
      year={2021},
      eprint={2108.08224},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@article{LIM20211748,
title = {Temporal Fusion Transformers for interpretable multi-horizon time series forecasting},
journal = {International Journal of Forecasting},
volume = {37},
number = {4},
pages = {1748-1764},
year = {2021},
issn = {0169-2070},
doi = {https://doi.org/10.1016/j.ijforecast.2021.03.012},
url = {https://www.sciencedirect.com/science/article/pii/S0169207021000637},
author = {Bryan Lim and Sercan Ö. Arık and Nicolas Loeff and Tomas Pfister},
keywords = {Deep learning, Interpretability, Time series, Multi-horizon forecasting, Attention mechanisms, Explainable AI},
abstract = {Multi-horizon forecasting often contains a complex mix of inputs – including static (i.e. time-invariant) covariates, known future inputs, and other exogenous time series that are only observed in the past – without any prior information on how they interact with the target. Several deep learning methods have been proposed, but they are typically ‘black-box’ models that do not shed light on how they use the full range of inputs present in practical scenarios. In this paper, we introduce the Temporal Fusion Transformer (TFT) – a novel attention-based architecture that combines high-performance multi-horizon forecasting with interpretable insights into temporal dynamics. To learn temporal relationships at different scales, TFT uses recurrent layers for local processing and interpretable self-attention layers for long-term dependencies. TFT utilizes specialized components to select relevant features and a series of gating layers to suppress unnecessary components, enabling high performance in a wide range of scenarios. On a variety of real-world datasets, we demonstrate significant performance improvements over existing benchmarks, and highlight three practical interpretability use cases of TFT.}
}

@article{ORESHKIN2021116918,
title = {N-BEATS neural network for mid-term electricity load forecasting},
journal = {Applied Energy},
volume = {293},
pages = {116918},
year = {2021},
issn = {0306-2619},
doi = {https://doi.org/10.1016/j.apenergy.2021.116918},
url = {https://www.sciencedirect.com/science/article/pii/S0306261921003986},
author = {Boris N. Oreshkin and Grzegorz Dudek and Paweł Pełka and Ekaterina Turkina},
keywords = {Mid-term load forecasting, Neural networks, Deep learning},
abstract = {This paper addresses the mid-term electricity load forecasting problem. Solving this problem is necessary for power system operation and planning as well as for negotiating forward contracts in deregulated energy markets. We show that our proposed deep neural network modeling approach based on the deep neural architecture is effective at solving the mid-term electricity load forecasting problem. Proposed neural network has high expressive power to solve non-linear stochastic forecasting problems with time series including trends, seasonality and significant random fluctuations. At the same time, it is simple to implement and train, it does not require signal preprocessing, and it is equipped with a forecast bias reduction mechanism. We compare our approach against ten baseline methods, including classical statistical methods, machine learning and hybrid approaches, on 35 monthly electricity demand time series for European countries. The empirical study shows that proposed neural network clearly outperforms all competitors in terms of both accuracy and forecast bias. Code is available here: https://github.com/boreshkinai/nbeats-midterm.}
}

@article{MAKRIDAKIS202054,
title = {The M4 Competition: 100,000 time series and 61 forecasting methods},
journal = {International Journal of Forecasting},
volume = {36},
number = {1},
pages = {54-74},
year = {2020},
note = {M4 Competition},
issn = {0169-2070},
doi = {https://doi.org/10.1016/j.ijforecast.2019.04.014},
url = {https://www.sciencedirect.com/science/article/pii/S0169207019301128},
author = {Spyros Makridakis and Evangelos Spiliotis and Vassilios Assimakopoulos},
keywords = {Forecasting competitions, M competitions, Forecasting accuracy, Prediction intervals, Time series methods, Machine learning methods, Benchmarking methods, Practice of forecasting},
abstract = {The M4 Competition follows on from the three previous M competitions, the purpose of which was to learn from empirical evidence both how to improve the forecasting accuracy and how such learning could be used to advance the theory and practice of forecasting. The aim of M4 was to replicate and extend the three previous competitions by: (a) significantly increasing the number of series, (b) expanding the number of forecasting methods, and (c) including prediction intervals in the evaluation process as well as point forecasts. This paper covers all aspects of M4 in detail, including its organization and running, the presentation of its results, the top-performing methods overall and by categories, its major findings and their implications, and the computational requirements of the various methods. Finally, it summarizes its main conclusions and states the expectation that its series will become a testing ground for the evaluation of new methods and the improvement of the practice of forecasting, while also suggesting some ways forward for the field.}
}

@misc{
benton2023deep,
title={Deep Probabilistic Time Series Forecasting over Long Horizons},
author={Gregory Benton and Nate Gruver and Wesley Maddox and Andrew Gordon Wilson},
year={2023},
url={https://openreview.net/forum?id=22h1XSEiN0}
}


@misc{oord2016wavenet,
      title={WaveNet: A Generative Model for Raw Audio}, 
      author={Aaron van den Oord and Sander Dieleman and Heiga Zen and Karen Simonyan and Oriol Vinyals and Alex Graves and Nal Kalchbrenner and Andrew Senior and Koray Kavukcuoglu},
      year={2016},
      eprint={1609.03499},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}

@Article{electronics8080876,
AUTHOR = {Wan, Renzhuo and Mei, Shuping and Wang, Jun and Liu, Min and Yang, Fan},
TITLE = {Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting},
JOURNAL = {Electronics},
VOLUME = {8},
YEAR = {2019},
NUMBER = {8},
ARTICLE-NUMBER = {876},
URL = {https://www.mdpi.com/2079-9292/8/8/876},
ISSN = {2079-9292},
ABSTRACT = {Multivariable time series prediction has been widely studied in power energy, aerology, meteorology, finance, transportation, etc. Traditional modeling methods have complex patterns and are inefficient to capture long-term multivariate dependencies of data for desired forecasting accuracy. To address such concerns, various deep learning models based on Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) methods are proposed. To improve the prediction accuracy and minimize the multivariate time series data dependence for aperiodic data, in this article, Beijing PM2.5 and ISO-NE Dataset are analyzed by a novel Multivariate Temporal Convolution Network (M-TCN) model. In this model, multi-variable time series prediction is constructed as a sequence-to-sequence scenario for non-periodic datasets. The multichannel residual blocks in parallel with asymmetric structure based on deep convolution neural network is proposed. The results are compared with rich competitive algorithms of long short term memory (LSTM), convolutional LSTM (ConvLSTM), Temporal Convolution Network (TCN) and Multivariate Attention LSTM-FCN (MALSTM-FCN), which indicate significant improvement of prediction accuracy, robust and generalization of our model.},
DOI = {10.3390/electronics8080876}
}

@Article{app10072322,
AUTHOR = {Lara-Benítez, Pedro and Carranza-García, Manuel and Luna-Romera, José M. and Riquelme, José C.},
TITLE = {Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting},
JOURNAL = {Applied Sciences},
VOLUME = {10},
YEAR = {2020},
NUMBER = {7},
ARTICLE-NUMBER = {2322},
URL = {https://www.mdpi.com/2076-3417/10/7/2322},
ISSN = {2076-3417},
ABSTRACT = {Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric companies can now use historical data to make informed decisions on energy production by forecasting the expected demand. Many deep learning models have been proposed to deal with these types of time series forecasting problems. Deep neural networks, such as recurrent or convolutional, can automatically capture complex patterns in time series data and provide accurate predictions. In particular, Temporal Convolutional Networks (TCN) are a specialised architecture that has advantages over recurrent networks for forecasting tasks. TCNs are able to extract long-term patterns using dilated causal convolutions and residual blocks, and can also be more efficient in terms of computation time. In this work, we propose a TCN-based deep learning model to improve the predictive performance in energy demand forecasting. Two energy-related time series with data from Spain have been studied: the national electric demand and the power demand at charging stations for electric vehicles. An extensive experimental study has been conducted, involving more than 1900 models with different architectures and parametrisations. The TCN proposal outperforms the forecasting accuracy of Long Short-Term Memory (LSTM) recurrent networks, which are considered the state-of-the-art in the field.},
DOI = {10.3390/app10072322}
}


@Article{s21020603,
AUTHOR = {Koh, Bee Hock David and Lim, Chin Leng Peter and Rahimi, Hasnae and Woo, Wai Lok and Gao, Bin},
TITLE = {Deep Temporal Convolution Network for Time Series Classification},
JOURNAL = {Sensors},
VOLUME = {21},
YEAR = {2021},
NUMBER = {2},
ARTICLE-NUMBER = {603},
URL = {https://www.mdpi.com/1424-8220/21/2/603},
PubMedID = {33467136},
ISSN = {1424-8220},
ABSTRACT = {A neural network that matches with a complex data function is likely to boost the classification performance as it is able to learn the useful aspect of the highly varying data. In this work, the temporal context of the time series data is chosen as the useful aspect of the data that is passed through the network for learning. By exploiting the compositional locality of the time series data at each level of the network, shift-invariant features can be extracted layer by layer at different time scales. The temporal context is made available to the deeper layers of the network by a set of data processing operations based on the concatenation operation. A matching learning algorithm for the revised network is described in this paper. It uses gradient routing in the backpropagation path. The framework as proposed in this work attains better generalization without overfitting the network to the data, as the weights can be pretrained appropriately. It can be used end-to-end with multivariate time series data in their raw form, without the need for manual feature crafting or data transformation. Data experiments with electroencephalogram signals and human activity signals show that with the right amount of concatenation in the deeper layers of the proposed network, it can improve the performance in signal classification.},
DOI = {10.3390/s21020603}
}

@INPROCEEDINGS{8940265,
  author={Liu, Yujie and Dong, Hongbin and Wang, Xingmei and Han, Shuang},
  booktitle={2019 IEEE/ACIS 18th International Conference on Computer and Information Science (ICIS)}, 
  title={Time Series Prediction Based on Temporal Convolutional Network}, 
  year={2019},
  volume={},
  number={},
  pages={300-305},
  keywords={Convolution;Feature extraction;Logic gates;Time series analysis;Task analysis;Predictive models;Recurrent neural networks;temporal convolutional network;time series prediction;gated linear units},
  doi={10.1109/ICIS46139.2019.8940265}}


@article{lecun2015deep,
  title={Deep learning},
  author={LeCun, Yann and Bengio, Yoshua and Hinton, Geoffrey},
  DOI={10.1038/nature14539.},
  journal={nature},
  volume={521},
  number={7553},
  pages={436--444},
  year={2015},
  publisher={Nature Publishing Group UK London}
}

@inproceedings{chen2016xgboost,
  title={Xgboost: A scalable tree boosting system},
  author={Chen, Tianqi and Guestrin, Carlos},
  DOI={10.1145/2939672.2939785},
  booktitle={Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining},
  pages={785--794},
  year={2016}
}

@article{wooldridge2015introductory,
  title={Introductory econometrics: A modern approach: Nelson Education},
  author={Wooldridge, Jeffrey M},
  DOI={10.4236/jemaa.2020.1311011},
  journal={Scarborough, ON, Canada},
  year={2015}
}

@book{giudici2005applied,
  title={Applied data mining: statistical methods for business and industry},
  author={Giudici, Paolo},
  DOI={10.4236/ojbm.2014.22011},
  year={2005},
  publisher={John Wiley \& Sons}
}

@book{tsay2005analysis,
  title={Analysis of financial time series},
  author={Tsay, Ruey S},
  year={2005},
  publisher={John wiley \& sons}
}

@article{shen2020hybrid,
  title={Hybrid multi-mode machine learning-based fault diagnosis strategies with application to aircraft gas turbine engines},
  author={Shen, Yanyan and Khorasani, Khashayar},
  DOI={10.1016/j.neunet.2020.07.001},
  journal={Neural Networks},
  volume={130},
  pages={126--142},
  year={2020},
  publisher={Elsevier}
}


@article{arumugam2023algorithmic,
  title={Algorithmic trading: Intraday profitability and trading behavior},
  author={Arumugam, Devika},
  DOI={10.1016/j.econmod.2023.106521},
  journal={Economic Modelling},
  volume={128},
  pages={106521},
  year={2023},
  publisher={Elsevier}
}

@article{chen2021implied,
  title={The implied arbitrage mechanism in financial markets},
  author={Chen, Shiyi and Chng, Michael T and Liu, Qingfu},
  journal={Journal of econometrics},
  volume={222},
  number={1},
  pages={468--483},
  year={2021},
  publisher={Elsevier}
}

@article{gupta2020selecting,
  title={Selecting stock pairs for pairs trading while incorporating lead--lag relationship},
  author={Gupta, Kartikay and Chatterjee, Niladri},
  journal={Physica A: Statistical Mechanics and its Applications},
  DOI={},
  volume={551},
  pages={124103},
  year={2020},
  publisher={Elsevier}
}

@InProceedings{10.1007/978-3-319-63645-0_16,
author="Gupta, Kartikay and Chatterjee, Niladri",
DOI={https://doi.org/10.1007/978-3-319-63645-0_16},
editor="Satapathy, Suresh Chandra
and Joshi, Amit",
title="Financial Time Series Clustering",
booktitle="Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 2",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="146--156",
isbn="978-3-319-63645-0"
}

@article{JMLR:v23:21-1177,
  author  = {Julien Herzen and Francesco LÃ¤ssig and Samuele Giuliano Piazzetta and Thomas Neuer and LÃ©o Tafti and Guillaume Raille and Tomas Van Pottelbergh and Marek Pasieka and Andrzej Skrodzki and Nicolas Huguenin and Maxime Dumonal and Jan KoÅ›cisz and Dennis Bader and FrÃ©dÃ©rick Gusset and Mounir Benheddi and Camila Williamson and Michal Kosinski and Matej Petrik and GaÃ«l Grosch},
  title   = {Darts: User-Friendly Modern Machine Learning for Time Series},
  journal = {Journal of Machine Learning Research},
  year    = {2022},
  volume  = {23},
  number  = {124},
  pages   = {1-6},
  url     = {http://jmlr.org/papers/v23/21-1177.html}
}

@article{XU2023107148,
title = {Improved pairs trading strategy using two-level reinforcement learning framework},
journal = {Engineering Applications of Artificial Intelligence},
volume = {126},
pages = {107148},
year = {2023},
issn = {0952-1976},
doi = {https://doi.org/10.1016/j.engappai.2023.107148},
url = {https://www.sciencedirect.com/science/article/pii/S0952197623013325},
author = {Zhizhao Xu and Chao Luo},
keywords = {Pair trading, Reinforcement learning, Time series, Deep learning},
abstract = {Pairs trading is a popular classic neutral trading strategy in financial market. Deep reinforcement learning (DRL) has been widely used to improve the performance of this strategy. However, most works primarily focused on setting trading signals, but ignored selecting appropriate trading pairs. In this paper, a novel two-level reinforcement learning framework is proposed, where both pair selection and trading thresholds setting are involved. For pair selection, an Extended Option-Critic (EOC) method is utilized, which allows the agent to select trading pair on non-fixed length of time intervals. For trading thresholds setting, a three-agent Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method is used for setting the opening and stop-loss thresholds as well as decide whether to trade. The simulation results in the Chinese futures market demonstrate that our proposed method achieves higher returns compared to traditional methods and popular reinforcement learning approaches.}
}

@inproceedings{bacon2017option,
  title={The option-critic architecture},
  author={Bacon, Pierre-Luc and Harb, Jean and Precup, Doina},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={31},
  number={1},
  year={2017}
}

@article{lowe2017multi,
  title={Multi-agent actor-critic for mixed cooperative-competitive environments},
  author={Lowe, Ryan and Wu, Yi I and Tamar, Aviv and Harb, Jean and Pieter Abbeel, OpenAI and Mordatch, Igor},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@misc{ansari2024chronos,
      title={Chronos: Learning the Language of Time Series}, 
      author={Abdul Fatir Ansari and Lorenzo Stella and Caner Turkmen and Xiyuan Zhang and Pedro Mercado and Huibin Shen and Oleksandr Shchur and Syama Sundar Rangapuram and Sebastian Pineda Arango and Shubham Kapoor and Jasper Zschiegner and Danielle C. Maddix and Michael W. Mahoney and Kari Torkkola and Andrew Gordon Wilson and Michael Bohlke-Schneider and Yuyang Wang},
      year={2024},
      eprint={2403.07815},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{he2015deep,
      title={Deep Residual Learning for Image Recognition}, 
      author={Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
      year={2015},
      eprint={1512.03385},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@article{souza2020bertimbau,
  title={BERTimbau: Pretrained BERT Models for Brazilian Portuguese},
  author={Souza, F{\'a}bio and Nogueira, Rodrigo and Lotufo, Roberto},
  journal={Brazilian Conference on Intelligent Systems},
  pages={403--417},
  year={2020},
  organization={Springer}
}

% Llama 3.1 Models paper
@misc{grattafiori2024llama3herdmodels,
      title={The Llama 3 Herd of Models}, 
      author={Aaron Grattafiori and Abhimanyu Dubey and Abhinav Jauhri and Abhinav Pandey and Abhishek Kadian and Ahmad Al-Dahle and Aiesha Letman and Akhil Mathur and Alan Schelten and Alex Vaughan and Amy Yang and Angela Fan and Anirudh Goyal and Anthony Hartshorn and Aobo Yang and Archi Mitra and Archie Sravankumar and Artem Korenev and Arthur Hinsvark and Arun Rao and Aston Zhang and Aurelien Rodriguez and Austen Gregerson and Ava Spataru and Baptiste Roziere and Bethany Biron and Binh Tang and Bobbie Chern and Charlotte Caucheteux and Chaya Nayak and Chloe Bi and Chris Marra and Chris McConnell and Christian Keller and Christophe Touret and Chunyang Wu and Corinne Wong and Cristian Canton Ferrer and Cyrus Nikolaidis and Damien Allonsius and Daniel Song and Danielle Pintz and Danny Livshits and Danny Wyatt and David Esiobu and Dhruv Choudhary and Dhruv Mahajan and Diego Garcia-Olano and Diego Perino and Dieuwke Hupkes and Egor Lakomkin and Ehab AlBadawy and Elina Lobanova and Emily Dinan and Eric Michael Smith and Filip Radenovic and Francisco Guzmán and Frank Zhang and Gabriel Synnaeve and Gabrielle Lee and Georgia Lewis Anderson and Govind Thattai and Graeme Nail and Gregoire Mialon and Guan Pang and Guillem Cucurell and Hailey Nguyen and Hannah Korevaar and Hu Xu and Hugo Touvron and Iliyan Zarov and Imanol Arrieta Ibarra and Isabel Kloumann and Ishan Misra and Ivan Evtimov and Jack Zhang and Jade Copet and Jaewon Lee and Jan Geffert and Jana Vranes and Jason Park and Jay Mahadeokar and Jeet Shah and Jelmer van der Linde and Jennifer Billock and Jenny Hong and Jenya Lee and Jeremy Fu and Jianfeng Chi and Jianyu Huang and Jiawen Liu and Jie Wang and Jiecao Yu and Joanna Bitton and Joe Spisak and Jongsoo Park and Joseph Rocca and Joshua Johnstun and Joshua Saxe and Junteng Jia and Kalyan Vasuden Alwala and Karthik Prasad and Kartikeya Upasani and Kate Plawiak and Ke Li and Kenneth Heafield and Kevin Stone and Khalid El-Arini and Krithika Iyer and Kshitiz Malik and Kuenley Chiu and Kunal Bhalla and Kushal Lakhotia and Lauren Rantala-Yeary and Laurens van der Maaten and Lawrence Chen and Liang Tan and Liz Jenkins and Louis Martin and Lovish Madaan and Lubo Malo and Lukas Blecher and Lukas Landzaat and Luke de Oliveira and Madeline Muzzi and Mahesh Pasupuleti and Mannat Singh and Manohar Paluri and Marcin Kardas and Maria Tsimpoukelli and Mathew Oldham and Mathieu Rita and Maya Pavlova and Melanie Kambadur and Mike Lewis and Min Si and Mitesh Kumar Singh and Mona Hassan and Naman Goyal and Narjes Torabi and Nikolay Bashlykov and Nikolay Bogoychev and Niladri Chatterji and Ning Zhang and Olivier Duchenne and Onur Çelebi and Patrick Alrassy and Pengchuan Zhang and Pengwei Li and Petar Vasic and Peter Weng and Prajjwal Bhargava and Pratik Dubal and Praveen Krishnan and Punit Singh Koura and Puxin Xu and Qing He and Qingxiao Dong and Ragavan Srinivasan and Raj Ganapathy and Ramon Calderer and Ricardo Silveira Cabral and Robert Stojnic and Roberta Raileanu and Rohan Maheswari and Rohit Girdhar and Rohit Patel and Romain Sauvestre and Ronnie Polidoro and Roshan Sumbaly and Ross Taylor and Ruan Silva and Rui Hou and Rui Wang and Saghar Hosseini and Sahana Chennabasappa and Sanjay Singh and Sean Bell and Seohyun Sonia Kim and Sergey Edunov and Shaoliang Nie and Sharan Narang and Sharath Raparthy and Sheng Shen and Shengye Wan and Shruti Bhosale and Shun Zhang and Simon Vandenhende and Soumya Batra and Spencer Whitman and Sten Sootla and Stephane Collot and Suchin Gururangan and Sydney Borodinsky and Tamar Herman and Tara Fowler and Tarek Sheasha and Thomas Georgiou and Thomas Scialom and Tobias Speckbacher and Todor Mihaylov and Tong Xiao and Ujjwal Karn and Vedanuj Goswami and Vibhor Gupta and Vignesh Ramanathan and Viktor Kerkez and Vincent Gonguet and Virginie Do and Vish Vogeti and Vítor Albiero and Vladan Petrovic and Weiwei Chu and Wenhan Xiong and Wenyin Fu and Whitney Meers and Xavier Martinet and Xiaodong Wang and Xiaofang Wang and Xiaoqing Ellen Tan and Xide Xia and Xinfeng Xie and Xuchao Jia and Xuewei Wang and Yaelle Goldschlag and Yashesh Gaur and Yasmine Babaei and Yi Wen and Yiwen Song and Yuchen Zhang and Yue Li and Yuning Mao and Zacharie Delpierre Coudert and Zheng Yan and Zhengxing Chen and Zoe Papakipos and Aaditya Singh and Aayushi Srivastava and Abha Jain and Adam Kelsey and Adam Shajnfeld and Adithya Gangidi and Adolfo Victoria and Ahuva Goldstand and Ajay Menon and Ajay Sharma and Alex Boesenberg and Alexei Baevski and Allie Feinstein and Amanda Kallet and Amit Sangani and Amos Teo and Anam Yunus and Andrei Lupu and Andres Alvarado and Andrew Caples and Andrew Gu and Andrew Ho and Andrew Poulton and Andrew Ryan and Ankit Ramchandani and Annie Dong and Annie Franco and Anuj Goyal and Aparajita Saraf and Arkabandhu Chowdhury and Ashley Gabriel and Ashwin Bharambe and Assaf Eisenman and Azadeh Yazdan and Beau James and Ben Maurer and Benjamin Leonhardi and Bernie Huang and Beth Loyd and Beto De Paola and Bhargavi Paranjape and Bing Liu and Bo Wu and Boyu Ni and Braden Hancock and Bram Wasti and Brandon Spence and Brani Stojkovic and Brian Gamido and Britt Montalvo and Carl Parker and Carly Burton and Catalina Mejia and Ce Liu and Changhan Wang and Changkyu Kim and Chao Zhou and Chester Hu and Ching-Hsiang Chu and Chris Cai and Chris Tindal and Christoph Feichtenhofer and Cynthia Gao and Damon Civin and Dana Beaty and Daniel Kreymer and Daniel Li and David Adkins and David Xu and Davide Testuggine and Delia David and Devi Parikh and Diana Liskovich and Didem Foss and Dingkang Wang and Duc Le and Dustin Holland and Edward Dowling and Eissa Jamil and Elaine Montgomery and Eleonora Presani and Emily Hahn and Emily Wood and Eric-Tuan Le and Erik Brinkman and Esteban Arcaute and Evan Dunbar and Evan Smothers and Fei Sun and Felix Kreuk and Feng Tian and Filippos Kokkinos and Firat Ozgenel and Francesco Caggioni and Frank Kanayet and Frank Seide and Gabriela Medina Florez and Gabriella Schwarz and Gada Badeer and Georgia Swee and Gil Halpern and Grant Herman and Grigory Sizov and Guangyi and Zhang and Guna Lakshminarayanan and Hakan Inan and Hamid Shojanazeri and Han Zou and Hannah Wang and Hanwen Zha and Haroun Habeeb and Harrison Rudolph and Helen Suk and Henry Aspegren and Hunter Goldman and Hongyuan Zhan and Ibrahim Damlaj and Igor Molybog and Igor Tufanov and Ilias Leontiadis and Irina-Elena Veliche and Itai Gat and Jake Weissman and James Geboski and James Kohli and Janice Lam and Japhet Asher and Jean-Baptiste Gaya and Jeff Marcus and Jeff Tang and Jennifer Chan and Jenny Zhen and Jeremy Reizenstein and Jeremy Teboul and Jessica Zhong and Jian Jin and Jingyi Yang and Joe Cummings and Jon Carvill and Jon Shepard and Jonathan McPhie and Jonathan Torres and Josh Ginsburg and Junjie Wang and Kai Wu and Kam Hou U and Karan Saxena and Kartikay Khandelwal and Katayoun Zand and Kathy Matosich and Kaushik Veeraraghavan and Kelly Michelena and Keqian Li and Kiran Jagadeesh and Kun Huang and Kunal Chawla and Kyle Huang and Lailin Chen and Lakshya Garg and Lavender A and Leandro Silva and Lee Bell and Lei Zhang and Liangpeng Guo and Licheng Yu and Liron Moshkovich and Luca Wehrstedt and Madian Khabsa and Manav Avalani and Manish Bhatt and Martynas Mankus and Matan Hasson and Matthew Lennie and Matthias Reso and Maxim Groshev and Maxim Naumov and Maya Lathi and Meghan Keneally and Miao Liu and Michael L. Seltzer and Michal Valko and Michelle Restrepo and Mihir Patel and Mik Vyatskov and Mikayel Samvelyan and Mike Clark and Mike Macey and Mike Wang and Miquel Jubert Hermoso and Mo Metanat and Mohammad Rastegari and Munish Bansal and Nandhini Santhanam and Natascha Parks and Natasha White and Navyata Bawa and Nayan Singhal and Nick Egebo and Nicolas Usunier and Nikhil Mehta and Nikolay Pavlovich Laptev and Ning Dong and Norman Cheng and Oleg Chernoguz and Olivia Hart and Omkar Salpekar and Ozlem Kalinli and Parkin Kent and Parth Parekh and Paul Saab and Pavan Balaji and Pedro Rittner and Philip Bontrager and Pierre Roux and Piotr Dollar and Polina Zvyagina and Prashant Ratanchandani and Pritish Yuvraj and Qian Liang and Rachad Alao and Rachel Rodriguez and Rafi Ayub and Raghotham Murthy and Raghu Nayani and Rahul Mitra and Rangaprabhu Parthasarathy and Raymond Li and Rebekkah Hogan and Robin Battey and Rocky Wang and Russ Howes and Ruty Rinott and Sachin Mehta and Sachin Siby and Sai Jayesh Bondu and Samyak Datta and Sara Chugh and Sara Hunt and Sargun Dhillon and Sasha Sidorov and Satadru Pan and Saurabh Mahajan and Saurabh Verma and Seiji Yamamoto and Sharadh Ramaswamy and Shaun Lindsay and Shaun Lindsay and Sheng Feng and Shenghao Lin and Shengxin Cindy Zha and Shishir Patil and Shiva Shankar and Shuqiang Zhang and Shuqiang Zhang and Sinong Wang and Sneha Agarwal and Soji Sajuyigbe and Soumith Chintala and Stephanie Max and Stephen Chen and Steve Kehoe and Steve Satterfield and Sudarshan Govindaprasad and Sumit Gupta and Summer Deng and Sungmin Cho and Sunny Virk and Suraj Subramanian and Sy Choudhury and Sydney Goldman and Tal Remez and Tamar Glaser and Tamara Best and Thilo Koehler and Thomas Robinson and Tianhe Li and Tianjun Zhang and Tim Matthews and Timothy Chou and Tzook Shaked and Varun Vontimitta and Victoria Ajayi and Victoria Montanez and Vijai Mohan and Vinay Satish Kumar and Vishal Mangla and Vlad Ionescu and Vlad Poenaru and Vlad Tiberiu Mihailescu and Vladimir Ivanov and Wei Li and Wenchen Wang and Wenwen Jiang and Wes Bouaziz and Will Constable and Xiaocheng Tang and Xiaojian Wu and Xiaolan Wang and Xilun Wu and Xinbo Gao and Yaniv Kleinman and Yanjun Chen and Ye Hu and Ye Jia and Ye Qi and Yenda Li and Yilin Zhang and Ying Zhang and Yossi Adi and Youngjin Nam and Yu and Wang and Yu Zhao and Yuchen Hao and Yundi Qian and Yunlu Li and Yuzi He and Zach Rait and Zachary DeVito and Zef Rosnbrick and Zhaoduo Wen and Zhenyu Yang and Zhiwei Zhao and Zhiyu Ma},
      year={2024},
      eprint={2407.21783},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2407.21783}, 
}
% Transformer-based architectures for time series forecasting

    % Architecture Chronos is based on
@misc{raffel2023exploringlimitstransferlearning,
      title={Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, 
      author={Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
      year={2023},
      eprint={1910.10683},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1910.10683}, 
}

    % Chronos paper
@misc{ansari2024chronoslearninglanguagetime,
      title={Chronos: Learning the Language of Time Series}, 
      author={Abdul Fatir Ansari and Lorenzo Stella and Caner Turkmen and Xiyuan Zhang and Pedro Mercado and Huibin Shen and Oleksandr Shchur and Syama Sundar Rangapuram and Sebastian Pineda Arango and Shubham Kapoor and Jasper Zschiegner and Danielle C. Maddix and Hao Wang and Michael W. Mahoney and Kari Torkkola and Andrew Gordon Wilson and Michael Bohlke-Schneider and Yuyang Wang},
      year={2024},
      eprint={2403.07815},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2403.07815}, 
}

    % PatchTST, uses patches, which is what Chronos-bolt and Toto use
@misc{nie2023timeseriesworth64,
      title={A Time Series is Worth 64 Words: Long-term Forecasting with Transformers}, 
      author={Yuqi Nie and Nam H. Nguyen and Phanwadee Sinthong and Jayant Kalagnanam},
      year={2023},
      eprint={2211.14730},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2211.14730}, 
}

@misc{cohen2024toto,
  title={Toto: Time Series Optimized Transformer for Observability}, 
  author={Cohen, Ben and Khwaja, Emaad and Wang, Kan and Masson, Charles and Ramé, Elise and Doubli, Youssef and Abou-Amal, Othmane},
  year={2024},
  eprint={2407.07874},
  archivePrefix={arXiv},
  primaryClass={cs.LG}
}

@misc{cohen2025timedifferentobservabilityperspective,
      title={This Time is Different: An Observability Perspective on Time Series Foundation Models}, 
      author={Ben Cohen and Emaad Khwaja and Youssef Doubli and Salahidine Lemaachi and Chris Lettieri and Charles Masson and Hugo Miccinilli and Elise Ramé and Qiqi Ren and Afshin Rostamizadeh and Jean Ogier du Terrail and Anna-Monica Toon and Kan Wang and Stephan Xie and Zongzhe Xu and Viktoriya Zhukova and David Asker and Ameet Talwalkar and Othmane Abou-Amal},
      year={2025},
      eprint={2505.14766},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2505.14766}, 
}

@article{pedregosa2011scikit,
  title={Scikit-learn: Machine learning in Python},
  author={Pedregosa, Faustin and Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and others},
  journal={Journal of machine learning research},
  volume={12},
  pages={2825--2830},
  year={2011}
}

@article{das2023timesfm,
  title={A decoder-only foundation model for time-series forecasting},
  author={Das, Abhimanyu and Kong, Weihao and Sen, Rajat and Zhou, Yichen},
  journal={arXiv preprint arXiv:2310.10688},
  year={2023},
  url={https://arxiv.org/abs/2310.10688}
}

@article{zuo2024autotimes,
  title={AutoTimes: A foundation model for time-series forecasting},
  author={Zuo, Zhen and Liu, Zhenjing and Zheng, Zhen and Huang, Ziyang and Zhu, Siying and Yu, Zhongzhong and Kwok, James T},
  journal={arXiv preprint arXiv:2401.12345},
  year={2024},
  url={https://arxiv.org/abs/2401.12345}
}

@Article{forecast6020024,
AUTHOR = {Hadad, Eli and Hodarkar, Sohail and Lemeneh, Beakal and Shasha, Dennis},
TITLE = {Machine Learning-Enhanced Pairs Trading},
JOURNAL = {Forecasting},
VOLUME = {6},
YEAR = {2024},
NUMBER = {2},
PAGES = {434--455},
URL = {https://www.mdpi.com/2571-9394/6/2/24},
ISSN = {2571-9394},
ABSTRACT = {Forecasting returns in financial markets is notoriously challenging due to the resemblance of price changes to white noise. In this paper, we propose novel methods to address this challenge. Employing high-frequency Brazilian stock market data at one-minute granularity over a full year, we apply various statistical and machine learning algorithms, including Bidirectional Long Short-Term Memory (BiLSTM) with attention, Transformers, N-BEATS, N-HiTS, Convolutional Neural Networks (CNNs), and Temporal Convolutional Networks (TCNs) to predict changes in the price ratio of closely related stock pairs. Our findings indicate that a combination of reversion and machine learning-based forecasting methods yields the highest profit-per-trade. Additionally, by allowing the model to abstain from trading when the predicted magnitude of change is small, profits per trade can be further increased. Our proposed forecasting approach, utilizing a blend of methods, demonstrates superior accuracy compared to individual methods for high-frequency data.},
DOI = {10.3390/forecast6020024}
}


@misc{massive,
  author       = {{Massive}},
  title        = {Massive: Real-time and Historical Market Data APIs},
  year         = {2025},
  url          = {https://massive.com/},
  note         = {Accessed: 2025-08-29}
}

@misc{lsethomsonreuters,
      title        = {LSE Thomson Reuters Newswire Service}, 
      author       = {{London Stock Exchange Group and Thomson Reuters}},
      year         = {2025},
      url          = {https://www.lseg.com}, 
      note         = {Accessed: 2025-08-29}
}

@misc{yahoofinance,
  author       = {{Yahoo Finance}},
  title        = {Yahoo Finance},
  howpublished = {\url{https://finance.yahoo.com/}},
  note         = {Accessed: August 29, 2025}
}

@article{f_kelly_assets,
author = {Davis, Mark and Lleo, Sebastien},
year = {2011},
month = {02},
pages = {},
title = {Fractional Kelly Strategies for Benchmarked Asset Management},
isbn = {978-981-4293-49-5},
doi = {10.1142/9789814293501_0027}
}

@misc{brockwell2021fractionalgrowthportfolioinvestment,
      title={Fractional Growth Portfolio Investment}, 
      author={Anthony E. Brockwell},
      year={2021},
      eprint={2109.10814},
      archivePrefix={arXiv},
      primaryClass={q-fin.PM},
      url={https://arxiv.org/abs/2109.10814}, 
}

@TechReport{repec:snb:snbwpa:2025-11,
type={Working Papers},
institution={Swiss National Bank},
author={Daniele Ballinari and Jessica Maly},
title={FX sentiment analysis with large language models},
year={2025},
number={2025-11},
abstract={We enhance sentiment analysis in the foreign exchange (FX) market by fine-tuning large language models (LLMs) to better understand and interpret the complex language specific to FX markets. We build on existing methods by using state-of-the-art open source LLMs, fine-tuning them with labelled FX news articles and then comparing their performance against traditional approaches and alternative models. Furthermore, we tested these fine-tuned LLMs by creating investment strategies based on the sentiment they detect in FX analysis articles with the goal of demonstrating how well these strategies perform in real-world trading scenarios. Our findings indicate that the fine-tuned LLMs outperform the existing methods in terms of both the classification accuracy and trading performance, highlighting their potential for improving FX market sentiment analysis and investment decision-making.},
keywords={Large language models; Sentiment analysis; Fine-tuning; Text classification; Natural language proces},
doi={None},
url={https://ideas.repec.org/p/snb/snbwpa/2025-11.html},
}



% ---------- ARFIMA REFERENCES -------------------
@article{granger1980introduction,
  title={An introduction to long-memory time series models and fractional differencing},
  author={Granger, Clive WJ and Joyeux, Roselyne},
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}
@misc{hosking1981fractional,
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}

@book{beran2017statistics,
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  year={2017},
  publisher={Routledge}
}

@article{dowe2025novel,
  title={A novel arfima-ann hybrid model for forecasting time series—and its role in explainable ai},
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  volume={5},
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  year={2025}
}

@article{petropoulos2022forecasting,
  title={Forecasting: theory and practice},
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  journal={International Journal of forecasting},
  volume={38},
  number={3},
  pages={705--871},
  year={2022},
  publisher={Elsevier}
}

@article{belbute2017global,
  title={Do global CO2 emissions from fossil-fuel consumption exhibit long memory? A fractional-integration analysis},
  author={Belbute, Jos{\'e} M and Pereira, Alfredo M},
  journal={Applied Economics},
  volume={49},
  number={40},
  pages={4055--4070},
  year={2017},
  publisher={Taylor \& Francis}
}

@article{baillie1996long,
  title={Long memory processes and fractional integration in econometrics},
  author={Baillie, Richard T},
  journal={Journal of econometrics},
  volume={73},
  number={1},
  pages={5--59},
  year={1996},
  publisher={Elsevier}
}



% ---------- END OF ARFIMA REFERENCES -------------------

@book{efron1993introduction,
  title={An Introduction to the Bootstrap},
  author={Efron, Bradley and Tibshirani, Robert J.},
  year={1993},
  publisher={Chapman \& Hall/CRC}
}

@book{good2005permutation,
  title={Permutation, Parametric, and Bootstrap Tests of Hypotheses},
  author={Good, Phillip},
  year={2005},
  publisher={Springer Series in Statistics}
}

@article{maclean2010good,
  title={Good and bad properties of the Kelly criterion},
  author={MacLean, Leonard C and Thorp, Edward O and Ziemba, William T},
  journal={Risk},
  volume={20},
  number={2},
  pages={1},
  year={2010}
}

@misc{kingma2017adammethodstochasticoptimization,
      title={Adam: A Method for Stochastic Optimization}, 
      author={Diederik P. Kingma and Jimmy Ba},
      year={2017},
      eprint={1412.6980},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1412.6980}, 
}