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

TL;DR

This paper introduces a novel image fusion framework transforming multi-dimensional time-series data into 2D images, enabling automatic feature extraction and classification with high accuracy across various applications.

Contribution

It proposes a unified coding framework with new transform coding methods that enhance multi-domain representation for multi-dimensional time-series classification.

Findings

Achieved 100% accuracy in Parkinson's disease diagnosis

Attained 92.86% accuracy in fault detection

Reached 99.70% accuracy in action recognition

Abstract

Multi-Dimensional time series classification and prediction has been widely used in many fields, such as disease prevention, fault diagnosis and action recognition. However, the traditional method needs manual intervention and inference, and cannot realize the figurative expression of multi-Dimensional data, which lead to inadequate information mining. Inspired by the strong power of deep learning technology in image processing, we propose a unified time-series image fusion framework to transform multi-modal data into 2D-image, and then realize automatic feature extraction and classification based on a lightweight convolutional neural network. We present two basic image coding methods, Gray image coding, RGB image coding, and their step coding methods. Considering the universality of different application fields, we extended the coding method and propose two types transform coding, Transform-RGB coding and RGB-Transform coding, to improve the multi-domain representation ability. By applying to three typical scenes of Parkinson's disease diagnosis, bearing fault detection and gymnastics action recognition, we obtained the highest classification accuracy of 100%, 92.86% and 99.70% respectively, which were all higher than the classical processing methods. It proves the strong classification ability and universality of our coding framework to different multi-dimensional scenes. We expect that this method can be used and perform well in other scenarios, and be potential to facilitate the progress of related technology.