Visual Time Series Forecasting: An Image-driven Approach

Naftali Cohen, Srijan Sood, Zhen Zeng, Tucker Balch and Manuela Veloso

Papers from arXiv.org

Abstract: In this work, we address time-series forecasting as a computer vision task. We capture input data as an image and train a model to produce the subsequent image. This approach results in predicting distributions as opposed to pointwise values. To assess the robustness and quality of our approach, we examine various datasets and multiple evaluation metrics. Our experiments show that our forecasting tool is effective for cyclic data but somewhat less for irregular data such as stock prices. Importantly, when using image-based evaluation metrics, we find our method to outperform various baselines, including ARIMA, and a numerical variation of our deep learning approach.

Date: 2021-07, Revised 2021-11
New Economics Papers: this item is included in nep-big, nep-cmp and nep-for
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