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.Comment: 25 pages, 12 figure
