A Novel and Robust Approach of Time Series-to-Image Encoding for Classification Using Convolutional Neural Networks

In recent decades, the data collection technologies have evolved to facilitate the monitoring and improvement of numerous activities and processes in everyday human life. The evolution is propelled by the advancement of artificial intelligence (AI), which aims to emulate human intelligence in the execution of related tasks. The remarkable success of deep learning (DL) and computer vision (CV) on image data prompted researchers to consider its application to time series and multivariate data. In this context, time-series imaging has been identified as the research field for the transformation of time-series data (one-dimensional data format) into images (two-dimensional data format). State-of-art techniques of time-series imaging are Recurrence Plot (RP), Gramian Angular Field (GAF), and Markov Transition Field (MTF). This paper proposes a novel, robust and simple technique of time-series imaging using Grayscale Fingerprint Features Field Imaging (G3FI). The novel technique is distinguished by its low resolution of the resulting image and the simplicity of the transformation procedure. The efficacy of the novel and state-of-the-art techniques for enhancing the performance of CNN-based classification models on time-series datasets is thoroughly examined and compared.