# Predicting the Future is like Completing a Painting!

arXiv: https://arxiv.org/abs/2011.04750 · HTML: https://mlsanity.com/paper/2011.04750/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [TimeOmni-VL: Unified Models for Time Series Understanding and Generation](https://mlsanity.com/paper/2602.17149/) (2026)
- [HySim: An Efficient Hybrid Similarity Measure for Patch Matching in Image Inpainting](https://mlsanity.com/paper/2403.14292/) (2024)
- [Hinge-FM2I: An Approach using Image Inpainting for Interpolating Missing Data in Univariate Time Series](https://mlsanity.com/paper/2406.06631/) (2024)

## Abstract (arXiv)

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 problem, i.e., completing missing data in the image. An extensive experimental evaluation is conducted using a large dataset proposed by the well-known M3-competition. Results show that FM2I represents an efficient and robust tool for time series forecasting. It has achieved prominent results in terms of accuracy and outperforms the best M3 forecasting methods.