Predicting the Future is Like Completing a Painting: Towards a Novel Method for Time-Series Forecasting

This article is an introductory work towards a larger research framework relative to Scientific Prediction. It is a mixed between science and philosophy of science, therefore we can talk about Experimental Philosophy of Science. As a first result, we introduce a new forecasting method based on image completion, named Forecasting Method by Image Inpainting (FM2I). In fact, time series forecasting is transformed into fully images- and signal-based processing procedures. After transforming a time series data into its corresponding image, the problem of data forecasting becomes essentially a problem of image inpainting, i.e., completing missing data in the image. Extensive experimental evaluation was conducted using the shortest series of the dataset proposed by the well-known M3-competition. Results show that FM2I, despite still being in its infancy, represents an efficient and robust tool for short-term time series forecasting. It has achieved prominent results in terms of accuracy and outperforms the best M3 methods. We have also investigated the effectiveness of the FM2I against the Smyl method, the winner of the M4 competition. Using the same category of shortest series, results show a close accuracy compared to the Smyl method. The FM2I is also able to generate ensemble data forecasts, which contain the best and more accurate forecast compared to existing and considered methods.

Automated summary

This article introduces a new forecasting method called FM2I, based on image completion, for time series forecasting. Extensive experimental evaluation shows that FM2I is efficient and accurate in short-term time series forecasting, outperforming methods from M3 and M4 competitions in terms of accuracy.