Deep learning for time series classification a review.pdf
Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful applications in the last years. DNNs have indeed revolutionized the field of computer vision especially with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and audio can also be processed with DNNs to reach state of the art performance for document classification and speech recognition. In this article, we study the current state of the art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for TSC. We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR archive) and 12 multivariate time series datasets. By training 8,730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.
- 基于深度学习的时间序列分类综述代码运行步骤（Deep-learning-for-time-series-classification-a-review） 10332020-06-12准备工作 （1）github上下载代码，网址是：https://github.com/hfawaz/dl-4-tsc （2）pycharm导入下载好的代码，十分简单，结果如下图。其中readme文件必读，里面有关于代码的信息，以及实验的结果表格。 注意事项： （1）代码中的文件位置设置自行完成（根据你数据集的位置设置） （2）代码中使用的时间序列数据集： 单变量：http://timeseriesclassification.com/TSC.zip 多变量：http://www.mustafabaydo.
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