Multi-Scale Convolutional Neural Network for Time Series Classification (MCNN)

Zhicheng Cui Wenlin Chen Yixin Chen

architecture

Introduction

Time series classification (TSC), the problem of predicting class labels of time series, has been around for decades within the community of data mining and machine learning, and found many important applications such as biomedical engineering and clinical prediction. However, it still remains challenging and falls short of classification accuracy and efficiency. Traditional approaches typically involve extracting discriminative features from the original time series using dynamic time warping (DTW) or shapelet transformation, based on which an off-the-shelf classifier can be applied. These methods are ad-hoc and separate the feature extraction part with the classification part, which limits their accuracy performance. Plus, most existing methods fail to take into account the fact that time series often have features at different time scales. To address these problems, we propose a novel end-to-end neural network model, Multi-Scale Convolutional Neural Networks (MCNN), which incorporates feature extraction and classification in a single framework. Leveraging a novel multi-branch layer and learnable convolutional layers, MCNN automatically extracts features at different scales and frequencies, leading to superior feature representation. MCNN is also computationally efficient, as it naturally leverages GPU computing. We conduct comprehensive empirical evaluation with various existing methods on a large number of benchmark datasets, and show that MCNN advances the state-of-the-art by achieving superior accuracy performance than other leading methods.

Python Code Download

For any questions or bug reports, please contact Zhicheng Cui (z.cui AT wustl.edu).

Reference

[1]. Z. Cui, W. Chen and Y. Chen, Multi-Scale Convolutional Neural Network for Time Series Classification, arXiv preprint arXiv:1603.06995, 2016. (paper)

Acknowledgement

This research is supported in part by the IIS-1343896, DBI-1356669, and III-1526012 grants from the National Science Foundation of the United States, a Microsoft Research New Faculty Fellowship, and a Barnes-Jewish Hospital Foun-dation grant.