Time Series Classification Using Multi-Channels Deep Convolutional Neural Networks

计算机科学 动态时间归整 人工智能 卷积神经网络 时间序列 模式识别(心理学) 人工神经网络 深度学习 机器学习 k-最近邻算法 多元统计 数据挖掘 系列(地层学) 循环神经网络 特征提取 特征(语言学) 上下文图像分类 频道(广播) 卷积(计算机科学) Softmax函数 哲学 古生物学 生物 语言学
作者
Yi Zheng,Qi Liu,Enhong Chen,Yong Ge,Jinxi Zhao
出处
期刊:Lecture Notes in Computer Science 卷期号:: 298-310 被引量:333
标识
DOI:10.1007/978-3-319-08010-9_33
摘要

Time series (particularly multivariate) classification has drawn a lot of attention in the literature because of its broad applications for different domains, such as health informatics and bioinformatics. Thus, many algorithms have been developed for this task. Among them, nearest neighbor classification (particularly 1-NN) combined with Dynamic Time Warping (DTW) achieves the state of the art performance. However, when data set grows larger, the time consumption of 1-NN with DTW grows linearly. Compared to 1-NN with DTW, the traditional feature-based classification methods are usually more efficient but less effective since their performance is usually dependent on the quality of hand-crafted features. To that end, in this paper, we explore the feature learning techniques to improve the performance of traditional feature-based approaches. Specifically, we propose a novel deep learning framework for multivariate time series classification. We conduct two groups of experiments on real-world data sets from different application domains. The final results show that our model is not only more efficient than the state of the art but also competitive in accuracy. It also demonstrates that feature learning is worth to investigate for time series classification.
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