Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Time Series Data Augmentation for Deep Learning: A Survey   Deep learning performs remarkably well on many time series analysis tasks
recently. The superior performance of deep neural networks relies heavily on a
large number of training data to avoid overfitting. However, the labeled data
of many real-world time series applications may be limited such as
classification in medical time series and anomaly detection in AIOps. As an
effective way to enhance the size and quality of the training data, data
augmentation is crucial to the successful application of deep learning models
on time series data. In this paper, we systematically review different data
augmentation methods for time series. We propose a taxonomy for the reviewed
methods, and then provide a structured review for these methods by highlighting
their strengths and limitations. We also empirically compare different data
augmentation methods for different tasks including time series classification,
anomaly detection, and forecasting. Finally, we discuss and highlight five
future directions to provide useful research guidance.

4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph