Pith. sign in

REVIEW 5 cited by

Time Series Data Augmentation for Deep Learning: A Survey

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2002.12478 v4 pith:YU7IUDUQ submitted 2020-02-27 cs.LG eess.SPstat.ML

Qingsong Wen , Liang Sun , Fan Yang , Xiaomin Song , Jingkun Gao , Xue Wang , Huan Xu This is my paper
classification cs.LGeess.SPstat.ML
keywords dataseriestimeaugmentationdeepmethodsdifferentlearning
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Sign in to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Text Reinforcement for Multimodal Time Series Forecasting

    cs.CL 2025-08 conditional novelty 6.0 of 10

    Reinforcement learning trains an LLM to generate improved text from time series, improving multimodal forecasting on Time-MMD.

  2. Time Series Foundation Models for Multivariate Financial Time Series Forecasting

    q-fin.GN 2025-07 reject novelty 6.0 of 10

    Pretrained TTM shows large transfer and sample-efficiency gains in three financial forecasting tasks relative to training from scratch, but methodological flaws including possible look-ahead bias weaken the quantitati...

  3. Spatiotemporal deep learning models for detection of rapid intensification in cyclones

    cs.LG 2025-06 conditional novelty 5.0 of 10

    Adding LSTM-generated synthetic cyclone tracks to training data improves deep learning detection of rapid intensification events, but the gain is not compared with simpler baselines.

  4. Discrepancy-Aware Contrastive Adaptation in Medical Time Series Analysis

    cs.HC 2025-08 reject novelty 4.0 of 10

    A three-stage pipeline that appends an autoencoder reconstruction error (trained on external normal recordings) to the input and trains attention-based multi-view contrastive representations, claiming SOTA on three EE...

  5. AI-in-the-Loop Sensing and Communication Joint Design for Edge Intelligence

    cs.LG 2025-02 conditional novelty 4.0 of 10

    An AI-in-the-loop JSAC framework tunes sensing and transmission to lower both resource costs and validation loss in federated edge learning.

Pith tools