551 citations · 2k across the 31 of their papers we have counts for
32 papers
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…
EasyTPP: Towards Open Benchmarking Temporal Point Processes
Siqiao Xue, Xiaoming Shi, Zhixuan Chu +9
Continuous-time event sequences play a vital role in real-world domains such as healthcare, finance, online shopping, social networks, and so on. To model such data, temporal point…
Bridging AI and Energy Forecasting: An Autonomous Workflow with Customized Toolkit
Zhixian Wang, Leandro Von Krannichfeldt, Qingsong Wen +4
Energy forecasting is crucial for the power grid, but fundamentally different from general time series analysis: it highly relies on covariates like meteorological factors, and its…
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
Ming Jin, Huan Yee Koh, Qingsong Wen +5
Time series are the primary data type used to record dynamic system measurements and generated in great volume by both physical sensors and online processes (virtual sensors). Time…
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…
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
Kexin Zhang, Qingsong Wen, Chaoli Zhang +8
Self-supervised learning (SSL) has recently achieved impressive performance on various time series tasks. The most prominent advantage of SSL is that it reduces the dependence on l…
DiffLoad: Uncertainty Quantification in Electrical Load Forecasting with the Diffusion Model
Zhixian Wang, Qingsong Wen, Chaoli Zhang +2
Electrical load forecasting plays a crucial role in decision-making for power systems, including unit commitment and economic dispatch. The integration of renewable energy sources…
CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting
Wang Xue, Tian Zhou, Qingsong Wen +3
Recent studies have demonstrated the great power of Transformer models for time series forecasting. One of the key elements that lead to the transformer's success is the channel-in…
Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting
Ming Jin, Guangsi Shi, Yuan-Fang Li +7
Time series forecasting has remained a focal point due to its vital applications in sectors such as energy management and transportation planning. Spectral-temporal graph neural ne…
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,…
AirFormer: Predicting Nationwide Air Quality in China with Transformers
Yuxuan Liang, Yutong Xia, Songyu Ke +5
Air pollution is a crucial issue affecting human health and livelihoods, as well as one of the barriers to economic and social growth. Forecasting air quality has become an increas…
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…
RobustScaler: QoS-Aware Autoscaling for Complex Workloads
Huajie Qian, Qingsong Wen, Liang Sun +3
Autoscaling is a critical component for efficient resource utilization with satisfactory quality of service (QoS) in cloud computing. This paper investigates proactive autoscaling…
A Global Modeling Approach for Load Forecasting in Distribution Networks
Miha Grabner, Yi Wang, Qingsong Wen +2
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 i…
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…