Department of Mathematical Sciences¹
University of Texas at El Paso
El Paso, Texas
PhD. In Data Science²
University of Texas at El Paso
El Paso, Texas
School of Theoretical and Applied Science³
Ramapo College of New Jersey
Mahwah, New Jersey
Title
Transforming Time Series into Texture Images: A Fusion of Recurrence Plots and Gramian Angular Fields for Convolutional Neural Network-Based Classification
Synopsis
This paper presents a novel approach for converting time series data into texture images by combining recurrence plots and Gramian angular fields, termed as Recurrence-Gramian Convolutional Neural Network (RGCNN). Recurrence plots capture the temporal dependencies and recurrence patterns inherent in time series data, while Gramian angular fields encode the temporal dynamics into spatial structures resembling texture images. By combining these two techniques, we aim to create rich representations of time series data that can be effectively utilized by CNNs for classification tasks.