MTMI-DCNN: A PSR-Based Method for Time Series Sensor Data Classification

Sensor data classification, a typical Time Series Classification (TSC) problem, spans many applications of sensors in the real world. How to improve classification performance is a challenging problem in these applications. A method based on Phase-Space Reconstruction (PSR), named MTMI-DCNN, is proposed for TSC. The basic idea of this method is to convert the time series to 2D images with the largest possible resolution by PSR, and then use Deep Convolutional Neural Network (DCNN) to classify these images. Three sequential stages are contained in this method: parameter optimization stage, time-series-imaging stage, and classification stage. In the parameter optimization stage, an optimization method is presented to optimize the reconstruction parameters just by maximizing the resolution of the image converted. In the time-series-imaging stage, the time series is embedded into a phase space using the optimal parameters, and the trajectory matrix of the time series is treated directly as an image for later classification. Consequently, no phase-space-trajectory projection is needed and the information loss caused by the projection is avoided. In the classification stage, the images converted from time series are classified by DCNN. Experimental results show that the proposed MTMI-DCNN outperforms other state-of-the-art methods on various datasets: the accuracies achieve 98.7%, 100.0%, and 82.6% on datasets UoB, Coffee, and CHMO, respectively; the Number of Wins and Average Error Rate achieve 19 and 1.41% respectively on 60 UTS datasets from the UCR archive. Hence, it can serve as an additional valuable method for TSC. Source code is available at
https://github.com/threedteam/MTMI-DCNN/
.