NOVEL APPROACH FOR IMAGING TIME SERIES FOR THE IMPROVEMENT OF CLASSIFICATION RESULTS “GRAYSCALE FINGERPRINT FEATURES FIELD IMAGING (G3FI)”

Abstract

The remarkable successes achieved by Deep Learning (DL) in computer vision drew the attention of researchers to exploit this capability to apply it for the processing of time series and multivariate data. Thus, researcher began the development of suitable methods to transform time series signals into, images to enable the use of DL techniques to improve the classification of time series data. The most state-of-art techniques are Recurrence Plot (RP), Gramian Angular Field (GAF), or Markov Transition Field (MTF). These techniques transform each time series into RGB image. In this paper, a new transformation technique of time series and multivariate data to images technique is proposed. This technique is called <Grayscale Fingerprint Features Field Imaging" (G3FI). The main differences between this technique and state-of-art techniques are: a) the resulted image is a grayscale; b) the size of the resulted image is much smaller than the size of resulted images using state-of-art techniques. These differences provide the proposed technique with several advantages over the prior art, as it (a) results in avoiding redundant information and (b) noise, and (c) leads to a significant reduction in the required computational power. For the proof of concept, a dataset "Sonar, Mines vs. Rocks" is investigated and individually transformed using RP, GAF, and MTF, and the new developed technique "G3FI". The resulted images from each transformer are individually used to train and test Convolutional Neural Networks (CNN). The proposed method leads to competitive results in comparison to RP, GAF, and MTF.