Search Results for author: Qingsong Wen

Found 17 papers, 7 papers with code

Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment

no code implementations24 Oct 2022 Chenxiao Yang, Qitian Wu, Qingsong Wen, Zhiqiang Zhou, Liang Sun, Junchi Yan

The goal of sequential event prediction is to estimate the next event based on a sequence of historical events, with applications to sequential recommendation, user behavior analysis and clinical treatment.

Sequential Recommendation Variational Inference

TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis

1 code implementation18 Oct 2022 Chaoli Zhang, Tian Zhou, Qingsong Wen, Liang Sun

Time series anomaly detection is a challenging problem due to the complex temporal dependencies and the limited label data.

Anomaly Detection Data Augmentation +1

TreeDRNet:A Robust Deep Model for Long Term Time Series Forecasting

no code implementations24 Jun 2022 Tian Zhou, Jianqing Zhu, Xue Wang, Ziqing Ma, Qingsong Wen, Liang Sun, Rong Jin

Various deep learning models, especially some latest Transformer-based approaches, have greatly improved the state-of-art performance for long-term time series forecasting. However, those transformer-based models suffer a severe deterioration performance with prolonged input length, which prohibits them from using extended historical info. Moreover, these methods tend to handle complex examples in long-term forecasting with increased model complexity, which often leads to a significant increase in computation and less robustness in performance(e. g., overfitting).

Time Series Forecasting

FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting

1 code implementation18 May 2022 Tian Zhou, Ziqing Ma, Xue Wang, Qingsong Wen, Liang Sun, Tao Yao, Wotao Yin, Rong Jin

Recent studies have shown that deep learning models such as RNNs and Transformers have brought significant performance gains for long-term forecasting of time series because they effectively utilize historical information.

Dimensionality Reduction Time Series Forecasting

A Global Modeling Approach for Load Forecasting in Distribution Networks

no code implementations1 Apr 2022 Miha Grabner, Yi Wang, Qingsong Wen, Boštjan Blažič, Vitomir Štruc

Efficient load forecasting is needed to ensure better observability in the distribution networks, whereas such forecasting is made possible by an increasing number of smart meter installations.

Load Forecasting

Transformers in Time Series: A Survey

2 code implementations15 Feb 2022 Qingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, Liang Sun

To the best of our knowledge, this paper is the first work to comprehensively and systematically summarize the recent advances of Transformers for modeling time series data.

Anomaly Detection Time Series

FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting

1 code implementation30 Jan 2022 Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, Rong Jin

Although Transformer-based methods have significantly improved state-of-the-art results for long-term series forecasting, they are not only computationally expensive but more importantly, are unable to capture the global view of time series (e. g. overall trend).

Time Series

CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms

no code implementations5 Nov 2021 Yingying Zhang, Zhengxiong Guan, Huajie Qian, Leili Xu, Hengbo Liu, Qingsong Wen, Liang Sun, Junwei Jiang, Lunting Fan, Min Ke

As business of Alibaba expands across the world among various industries, higher standards are imposed on the service quality and reliability of big data cloud computing platforms which constitute the infrastructure of Alibaba Cloud.

Anomaly Detection Transfer Learning

Two-Stage Framework for Seasonal Time Series Forecasting

no code implementations3 Mar 2021 Qingyang Xu, Qingsong Wen, Liang Sun

By incorporating the learned long-range structure, the second stage can enhance the prediction accuracy in the forecast horizon.

Time Series Forecasting

RobustPeriod: Time-Frequency Mining for Robust Multiple Periodicity Detection

1 code implementation21 Feb 2020 Qingsong Wen, Kai He, Liang Sun, Yingying Zhang, Min Ke, Huan Xu

Periodicity detection is a crucial step in time series tasks, including monitoring and forecasting of metrics in many areas, such as IoT applications and self-driving database management system.

Anomaly Detection Management +2

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