551 citations · 2k across the 30 of their papers we have counts for
26 papers · 1 filter
GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network
Weiqi Chen, Zhiqiang Zhou, Qingsong Wen +1
Time series subsequence anomaly detection is an important task in a large variety of real-world applications ranging from health monitoring to AIOps, and is challenging due to the…
Evolving Multi-Scale Normalization for Time Series Forecasting under Distribution Shifts
Dalin Qin, Yehui Li, Weiqi Chen +5
Complex distribution shifts are the main obstacle to achieving accurate long-term time series forecasting. Several efforts have been conducted to capture the distribution character…
RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies
Hao Cheng, Qingsong Wen, Yang Liu +1
Time series forecasting is an important and forefront task in many real-world applications. However, most of time series forecasting techniques assume that the training data is cle…
Interactive Generalized Additive Model and Its Applications in Electric Load Forecasting
Linxiao Yang, Rui Ren, Xinyue Gu +1
Electric load forecasting is an indispensable component of electric power system planning and management. Inaccurate load forecasting may lead to the threat of outages or a waste o…
OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling
Yi-Fan Zhang, Qingsong Wen, Xue Wang +6
Online updating of time series forecasting models aims to address the concept drifting problem by efficiently updating forecasting models based on streaming data. Many algorithms a…
BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition
Shikai Fang, Qingsong Wen, Yingtao Luo +2
In real-world scenarios like traffic and energy, massive time-series data with missing values and noises are widely observed, even sampled irregularly. While many imputation method…
SaDI: A Self-adaptive Decomposed Interpretable Framework for Electric Load Forecasting under Extreme Events
Hengbo Liu, Ziqing Ma, Linxiao Yang +5
Accurate prediction of electric load is crucial in power grid planning and management. In this paper, we solve the electric load forecasting problem under extreme events such as sc…
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection
Yiyuan Yang, Chaoli Zhang, Tian Zhou +2
Time series anomaly detection is critical for a wide range of applications. It aims to identify deviant samples from the normal sample distribution in time series. The most fundame…
GCformer: An Efficient Framework for Accurate and Scalable Long-Term Multivariate Time Series Forecasting
YanJun Zhao, Ziqing Ma, Tian Zhou +3
Transformer-based models have emerged as promising tools for time series forecasting. However, these model cannot make accurate prediction for long input time series. On the one ha…
AHPA: Adaptive Horizontal Pod Autoscaling Systems on Alibaba Cloud Container Service for Kubernetes
Zhiqiang Zhou, Chaoli Zhang, Lingna Ma +6
The existing resource allocation policy for application instances in Kubernetes cannot dynamically adjust according to the requirement of business, which would cause an enormous wa…
Robust Dominant Periodicity Detection for Time Series with Missing Data
Qingsong Wen, Linxiao Yang, Liang Sun
Periodicity detection is an important task in time series analysis, but still a challenging problem due to the diverse characteristics of time series data like abrupt trend change,…
One Fits All:Power General Time Series Analysis by Pretrained LM
Tian Zhou, PeiSong Niu, Xue Wang +2
Although we have witnessed great success of pre-trained models in natural language processing (NLP) and computer vision (CV), limited progress has been made for general time series…
Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment
Chenxiao Yang, Qitian Wu, Qingsong Wen +3
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 analys…
TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis
Chaoli Zhang, Tian Zhou, Qingsong Wen +1
Time series anomaly detection is a challenging problem due to the complex temporal dependencies and the limited label data. Although some algorithms including both traditional and…
TreeDRNet:A Robust Deep Model for Long Term Time Series Forecasting
Tian Zhou, Jianqing Zhu, Xue Wang +4
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,…
Robust Time Series Dissimilarity Measure for Outlier Detection and Periodicity Detection
Xiaomin Song, Qingsong Wen, Yan Li +1
Dynamic time warping (DTW) is an effective dissimilarity measure in many time series applications. Despite its popularity, it is prone to noises and outliers, which leads to singul…
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting
Tian Zhou, Ziqing Ma, Xue wang +5
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 e…
NetRCA: An Effective Network Fault Cause Localization Algorithm
Chaoli Zhang, Zhiqiang Zhou, Yingying Zhang +4
Localizing the root cause of network faults is crucial to network operation and maintenance. However, due to the complicated network architectures and wireless environments, as wel…
Transformers in Time Series: A Survey
Qingsong Wen, Tian Zhou, Chaoli Zhang +4
Transformers have achieved superior performances in many tasks in natural language processing and computer vision, which also triggered great interest in the time series community.…
FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting
Tian Zhou, Ziqing Ma, Qingsong Wen +3
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 impor…
Two-Stage Framework for Seasonal Time Series Forecasting
Qingyang Xu, Qingsong Wen, Liang Sun
Seasonal time series Forecasting remains a challenging problem due to the long-term dependency from seasonality. In this paper, we propose a two-stage framework to forecast univari…
Time Series Data Augmentation for Deep Learning: A Survey
Qingsong Wen, Liang Sun, Fan Yang +4
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 da…
RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks
Jingkun Gao, Xiaomin Song, Qingsong Wen +3
The monitoring and management of numerous and diverse time series data at Alibaba Group calls for an effective and scalable time series anomaly detection service. In this paper, we…
RobustPeriod: Time-Frequency Mining for Robust Multiple Periodicity Detection
Qingsong Wen, Kai He, Liang Sun +3
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 mana…