Visual Time Series Forecasting: An Image-driven Approach

TL;DR

This paper introduces a novel visual forecasting framework for time series data that leverages deep learning to produce image-based predictions, outperforming traditional numerical methods on certain datasets.

Contribution

The work presents a new image-driven approach to time series forecasting, extending the field by predicting visual representations instead of numerical values.

Findings

Effective for cyclic data

Less effective for irregular data like stock prices

Outperforms ARIMA and other numerical baselines

Abstract

Time series forecasting is essential for agents to make decisions. Traditional approaches rely on statistical methods to forecast given past numeric values. In practice, end-users often rely on visualizations such as charts and plots to reason about their forecasts. Inspired by practitioners, we re-imagine the topic by creating a novel framework to produce visual forecasts, similar to the way humans intuitively do. In this work, we leverage advances in deep learning to extend the field of time series forecasting to a visual setting. 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. We examine various synthetic and real datasets with diverse degrees of complexity. Our experiments show that visual forecasting is effective for cyclic data but somewhat less for irregular data such as stock price. Importantly, when using image-based evaluation metrics, we find the proposed visual forecasting method to outperform various numerical baselines, including ARIMA and a numerical variation of our method. We demonstrate the benefits of incorporating vision-based approaches in forecasting tasks -- both for the quality of the forecasts produced, as well as the metrics that can be used to evaluate them.