학술논문
Image Classification of Time Series Based on Deep Convolutional Neural Network
Document Type
Conference
Source
2021 40th Chinese Control Conference (CCC) Chinese Control Conference (CCC), 2021 40th. :8488-8491 Jul, 2021
Subject
Language
ISSN
1934-1768
Abstract
Convolutional Neural Networks (CNN) have achieved great success in image recognition tasks by automatically learning hierarchical feature representations from raw data. Most time series classification (TSC) mainly focuses on one-dimensional signals. In this paper, we plan to study high dimensional time series data. The main idea is following: Firstly, data enhancement is done which means that we use synthetic minority oversampling technique (SMOTE) to preprocess the arrhythmia data. Secondly, we use recurrence plot (RP) to convert the time series into two dimensional texture images. Thirdly, the deep CNN classifier is used for recognition. And finally, time series classification can be regarded as the texture image recognition task. The arrhythmia data is used to demonstrate the effectiveness of the proposed method for processing time series data sets.