[论文解读] Multivariate Time Series Classification using Dilated Convolutional Neural Network

Omolbanin Yazdanbakhsh, Scott Dick|arXiv (Cornell University)|May 5, 2019

Time Series Analysis and Forecasting参考文献 21被引用 34

一句话总结

本文提出一种基于扩张 CNN 的方法，将多变量时间序列段转换为单通道图像并进行分类，在 WISDM 数据集上取得与手工特征相当的结果。

ABSTRACT

Multivariate time series classification is a high value and well-known problem in machine learning community. Feature extraction is a main step in classification tasks. Traditional approaches employ hand-crafted features for classification while convolutional neural networks (CNN) are able to extract features automatically. In this paper, we use dilated convolutional neural network for multivariate time series classification. To deploy dilated CNN, a multivariate time series is transformed into an image-like style and stacks of dilated and strided convolutions are applied to extract in and between features of variates in time series simultaneously. We evaluate our model on two human activity recognition time series, finding that the automatic features extracted for the time series can be as effective as hand-crafted features.

研究动机与目标

• Motivate multivariate time series classification and reduce reliance on hand-crafted features.
• Propose an image-based representation of multivariate segments processed by dilated CNNs.
• Demonstrate that learned features can match hand-crafted spectrogram and statistical features on benchmark data.

提出的方法

• Convert multivariate time series segments into one-channel images with M rows and K columns.
• Apply stacks of dilated and strided convolutions to learn intra- and inter-variate relations.
• Use a fully connected classifier with softmax for final class probabilities.
• Train with cross-entropy loss using Adam optimizer and L2 regularization.

实验结果

研究问题

• RQ1 Can a dilated CNN operating on image-like representations of multivariate time series achieve competitive classification performance without hand-crafted features?
• RQ2 How well does the model capture inter-variate and temporal dependencies compared to prior approaches using spectrogram or hand-crafted features?
• RQ3 What is the impact of architecture choices (dilation, stride, layer depths) on recognition accuracy across activities?

主要发现

• On WISDM v1-split, our method achieves higher F1-scores than the cited spectrogram-based approach for Walking, Jogging, Sitting and Standing, with Upstairs and Downstairs being the weaker ones.
• On WISDM v1-individual, the method shows competitive recall across activities compared to prior CNN-based feature extraction methods.
• On WISDM v.2, the model matches or slightly trails the spectrogram-based method for Walking and Jogging, but outperforms for Sitting, Standing, and Lying Down.
• Overall, automatically learned features via the dilated CNN can be as effective as hand-crafted features in several activities, with some exceptions in stairs-related classes.