H
Huan Xu
Researcher at Alibaba Group
Publications - 12
Citations - 595
Huan Xu is an academic researcher from Alibaba Group. The author has contributed to research in topics: Time series & Series (mathematics). The author has an hindex of 5, co-authored 11 publications receiving 146 citations.
Papers
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Proceedings ArticleDOI
Time Series Data Augmentation for Deep Learning: A Survey
TL;DR: This paper systematically review different data augmentation methods for time series, and proposes a taxonomy for the reviewed methods, and provides a structured review for these methods by highlighting their strengths and limitations.
Proceedings ArticleDOI
Time Series Data Augmentation for Deep Learning: A Survey.
TL;DR: In this article, the authors systematically review different data augmentation methods for time series and provide a structured review for these methods by highlighting their strengths and limitations, and empirically compare different methods for different tasks including time series anomaly detection, classification, and forecasting.
Journal ArticleDOI
RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series
TL;DR: Wang et al. as discussed by the authors proposed a method to decompose complex time series into trend, seasonality, and remainder components, which can handle seasonality fluctuation and shift, and abrupt change in trend and reminder.
Posted Content
RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks.
TL;DR: This paper proposes RobustTAD, a Robust Time series Anomaly Detection framework by integrating robust seasonal-trend decomposition and convolutional neural network for time series data and introduces label-based weight and value- based weight in the loss function by utilizing the unbalanced nature of the time series anomaly detection problem.
Posted Content
RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series
TL;DR: This work proposes a novel and generic time series decomposition algorithm that extracts the trend component robustly by solving a regression problem using the least absolute deviations loss with sparse regularization and applies the non-local seasonal filtering to extract the seasonality component.