Classification of Time-Series Images Using Deep Convolutional Neural Networks
This addresses time-series classification for domains like signal processing by applying an image-based method, though it is incremental as it adapts existing techniques to a new representation.
The paper tackled time-series classification by transforming 1D signals into 2D recurrence plot images and using deep convolutional neural networks, achieving competitive accuracy on the UCR archive compared to state-of-the-art methods.
Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw data. While the majority of Time-Series Classification (TSC) literature is focused on 1D signals, this paper uses Recurrence Plots (RP) to transform time-series into 2D texture images and then take advantage of the deep CNN classifier. Image representation of time-series introduces different feature types that are not available for 1D signals, and therefore TSC can be treated as texture image recognition task. CNN model also allows learning different levels of representations together with a classifier, jointly and automatically. Therefore, using RP and CNN in a unified framework is expected to boost the recognition rate of TSC. Experimental results on the UCR time-series classification archive demonstrate competitive accuracy of the proposed approach, compared not only to the existing deep architectures, but also to the state-of-the art TSC algorithms.