Zhen Zeng, Rachneet Kaur, Suchetha Siddagangappa, Saba Rahimi, Tucker Balch, Manuela Veloso
arXiv 11 Apr 2023 · Machine Learning · 24 citations (OpenAlex)
arXiv:2304.04912 · PDF · DOI · OpenAlex · Extracted main text
Time series forecasting is important across various domains for decision-making. In particular, financial time series such as stock prices can be hard to predict as it is difficult to model short-term and long-term temporal dependencies between data points. Convolutional Neural Networks (CNN) are good at capturing local patterns for modeling short-term dependencies. However, CNNs cannot learn long-term dependencies due to the limited receptive field. Transformers on the other hand are capable of learning global context and long-term dependencies. In this paper, we propose to harness the power of CNNs and Transformers to model both short-term and long-term dependencies within a time series, and forecast if the price would go up, down or remain the same (flat) in the future. In our experiments, we demonstrated the success of the proposed method in comparison to commonly adopted statistical and deep learning methods on forecasting intraday stock price change of S&P 500 constituents.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gome… (2017) Attention is all you need | 0.843 | 3 | 3 | 100% |
| 2 | Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K (2018) Bert: Pre-training of deep bidirectional transformers for language understanding | 0.644 | 2 | 2 | 100% |
| 3 | Gardner Jr, E. S.; and McKenzie, E (1985) Forecasting trends in time series | 0.644 | 2 | 2 | 100% |
| 4 | Holt, C. C (2004) Forecasting seasonals and trends by exponentially weighted moving averages | 0.644 | 2 | 2 | 100% |
| 5 | Makridakis, S.; Spiliotis, E.; and Assimakopoulos, V (2020) The M4 Competition: 100,000 time series and 61 forecasting methods | 0.644 | 2 | 2 | 100% |
| 6 | Winters, P. R (1960) Forecasting sales by exponentially weighted moving averages | 0.644 | 2 | 2 | 100% |
| 7 | Sutskever, I.; Vinyals, O.; and Le, Q. V (2014) Sequence to sequence learning with neural networks | 0.405 | 1 | 1 | 100% |
| 8 | Bao, W.; Yue, J.; and Rao, Y (2017) A deep learning framework for financial time series using stacked autoencoders and long-short term memory | 0.405 | 1 | 1 | 100% |
| 9 | (2022) Bloomberg Market Data | 0.405 | 1 | 1 | 100% |
| 10 | Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J. D.; Dhariwal… (2020) Language models are few-shot learners | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 25 scored citations.