Data-Folding and Hyperspace Coding for Multi-Dimensonal Time-Series Data Imaging

Time-series classification and prediction are widely used in many applications. However, traditional machine learning algorithms, due to their limitations, have difficulty improving the performance of time-series classification and prediction. Inspired by the recent successes of the deep learning technology in computer vision, we develop a new time-series image encoding method for data reconstruction. Featuring data-folding and hyperspace coding this method breaks the barriers between time-series signals and images and establishes a close relationship between them, allowing effective application of the deep learning technology for time-series data. Besides a raw data coding method, we also present other four extended coding methods for other potential applications. For comparison purposes, we present the results of the five different types of image coding methods with our previous keystroke recognition datasets. The results show that our method can achieve an impressive accuracy of 96.27% when RGB coding images are used, and an accuracy of up to 97.33% when using radon coding way. We can expect that this method can also be used and perform well in other classification and prediction applications.