Zhen Zeng, Tucker Balch, Manuela Veloso
arXiv 24 Feb 2021 · cs.CV · 10 citations (OpenAlex)
arXiv:2102.12061 · PDF · DOI · OpenAlex · Extracted main text
Time series forecasting is essential for decision making in many domains. In this work, we address the challenge of predicting prices evolution among multiple potentially interacting financial assets. A solution to this problem has obvious importance for governments, banks, and investors. Statistical methods such as Auto Regressive Integrated Moving Average (ARIMA) are widely applied to these problems. In this paper, we propose to approach economic time series forecasting of multiple financial assets in a novel way via video prediction. Given past prices of multiple potentially interacting financial assets, we aim to predict the prices evolution in the future. Instead of treating the snapshot of prices at each time point as a vector, we spatially layout these prices in 2D as an image, such that we can harness the power of CNNs in learning a latent representation for these financial assets. Thus, the history of these prices becomes a sequence of images, and our goal becomes predicting future images. We build on a state-of-the-art video prediction method for forecasting future images. Our experiments involve the prediction task of the price evolution of nine financial assets traded in U.S. stock markets. The proposed method outperforms baselines including ARIMA, Prophet, and variations of the proposed method, demonstrating the benefits of harnessing the power of CNNs in the problem of economic time series forecasting.
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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 | Alok Sharma, Edwin Vans, Daichi Shigemizu, Keith A Boroevich, and Ta… (2019) DeepInsight: A methodology to transform a non-image data to an image for convolution neural network architecture | 1.000 | 9 | 4 | 100% |
| 2 | Jean-Yves Franceschi, Edouard Delasalles, Mickaël Chen, Sylvain Lamp… (2020) Stochastic Latent Residual Video Prediction | 1.000 | 5 | 3 | 100% |
| 3 | Naftali Cohen, Tucker Balch, and Manuela Veloso (2020) Trading via image classification. In Proceedings of the First ACM International Conference on AI in Finance. 1–6 self | 0.644 | 2 | 2 | 100% |
| 4 | Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton (2012) ImageNet Classification with Deep Convolutional Neural Networks | 0.405 | 1 | 1 | 100% |
| 5 | Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H Campbell, a… (2017) Stochastic variational video prediction | 0.405 | 1 | 1 | 100% |
| 6 | Emily Denton and Rob Fergus (2018) Stochastic video generation with a learned prior | 0.405 | 1 | 1 | 100% |
| 7 | Bairui Du and Paolo Barucca (2020) Image Processing Tools for Financial Time Series Classification | 0.405 | 1 | 1 | 100% |
| 8 | Frederik Ebert, Chelsea Finn, Alex X Lee, and Sergey Levine (2017) Self-supervised visual planning with temporal skip connections | 0.405 | 1 | 1 | 100% |
| 9 | Chelsea Finn, Ian Goodfellow, and Sergey Levine (2016) Unsupervised learning for physical interaction through video prediction. In Advances in neural information processing systems. 6… | 0.405 | 1 | 1 | 100% |
| 10 | Everette S Gardner Jr and ED McKenzie (1985) Forecasting trends in time series | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 26 scored citations.