Search Results for author: Qingsong Wen

Found 59 papers, 30 papers with code

Automate Knowledge Concept Tagging on Math Questions with LLMs

no code implementations26 Mar 2024 Hang Li, Tianlong Xu, Jiliang Tang, Qingsong Wen

Knowledge concept tagging for questions plays a crucial role in contemporary intelligent educational applications, including learning progress diagnosis, practice question recommendations, and course content organization.

Few-Shot Learning Math

Large Language Models for Education: A Survey and Outlook

no code implementations26 Mar 2024 Shen Wang, Tianlong Xu, Hang Li, Chaoli Zhang, Joleen Liang, Jiliang Tang, Philip S. Yu, Qingsong Wen

The advent of Large Language Models (LLMs) has brought in a new era of possibilities in the realm of education.

UrbanVLP: A Multi-Granularity Vision-Language Pre-Trained Foundation Model for Urban Indicator Prediction

no code implementations25 Mar 2024 Xixuan Hao, Wei Chen, Yibo Yan, Siru Zhong, Kun Wang, Qingsong Wen, Yuxuan Liang

Urban indicator prediction aims to infer socio-economic metrics in diverse urban landscapes using data-driven methods.

Text Generation

Foundation Models for Time Series Analysis: A Tutorial and Survey

no code implementations21 Mar 2024 Yuxuan Liang, Haomin Wen, Yuqi Nie, Yushan Jiang, Ming Jin, Dongjin Song, Shirui Pan, Qingsong Wen

Time series analysis stands as a focal point within the data mining community, serving as a cornerstone for extracting valuable insights crucial to a myriad of real-world applications.

Time Series Time Series Analysis

Online GNN Evaluation Under Test-time Graph Distribution Shifts

1 code implementation15 Mar 2024 Xin Zheng, Dongjin Song, Qingsong Wen, Bo Du, Shirui Pan

This enables the effective evaluation of the well-trained GNNs' ability to capture test node semantics and structural representations, making it an expressive metric for estimating the generalization error in online GNN evaluation.

Debiasing Multimodal Large Language Models

1 code implementation8 Mar 2024 Yi-Fan Zhang, Weichen Yu, Qingsong Wen, Xue Wang, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan

In the realms of computer vision and natural language processing, Large Vision-Language Models (LVLMs) have become indispensable tools, proficient in generating textual descriptions based on visual inputs.

Fairness Question Answering

PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations

no code implementations25 Feb 2024 shiyi qi, Zenglin Xu, Yiduo Li, Liangjian Wen, Qingsong Wen, Qifan Wang, Yuan Qi

Recent advancements in deep learning have led to the development of various models for long-term multivariate time-series forecasting (LMTF), many of which have shown promising results.

Multivariate Time Series Forecasting Time Series

Generative Semi-supervised Graph Anomaly Detection

1 code implementation19 Feb 2024 Hezhe Qiao, Qingsong Wen, XiaoLi Li, Ee-Peng Lim, Guansong Pang

This work considers a practical semi-supervised graph anomaly detection (GAD) scenario, where part of the nodes in a graph are known to be normal, contrasting to the unsupervised setting in most GAD studies with a fully unlabeled graph.

Graph Anomaly Detection One-class classifier

Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective

no code implementations18 Feb 2024 Jiaxi Hu, Yuehong Hu, Wei Chen, Ming Jin, Shirui Pan, Qingsong Wen, Yuxuan Liang

In long-term time series forecasting (LTSF) tasks, existing deep learning models overlook the crucial characteristic that discrete time series originate from underlying continuous dynamic systems, resulting in a lack of extrapolation and evolution capabilities.

Time Series Time Series Forecasting

Position Paper: What Can Large Language Models Tell Us about Time Series Analysis

3 code implementations5 Feb 2024 Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang, Bin Yang, Jindong Wang, Shirui Pan, Qingsong Wen

Time series analysis is essential for comprehending the complexities inherent in various real-world systems and applications.

Decision Making Position +3

Bringing Generative AI to Adaptive Learning in Education

no code implementations2 Feb 2024 Hang Li, Tianlong Xu, Chaoli Zhang, Eason Chen, Jing Liang, Xing Fan, Haoyang Li, Jiliang Tang, Qingsong Wen

The recent surge in generative AI technologies, such as large language models and diffusion models, have boosted the development of AI applications in various domains, including science, finance, and education.


Explaining Time Series via Contrastive and Locally Sparse Perturbations

1 code implementation16 Jan 2024 Zichuan Liu, Yingying Zhang, Tianchun Wang, Zefan Wang, Dongsheng Luo, Mengnan Du, Min Wu, Yi Wang, Chunlin Chen, Lunting Fan, Qingsong Wen

Explaining multivariate time series is a compound challenge, as it requires identifying important locations in the time series and matching complex temporal patterns.

Contrastive Learning counterfactual +1

Load Data Valuation in Multi-Energy Systems: An End-to-End Approach

no code implementations16 Nov 2023 Yangze Zhou, Qingsong Wen, Jie Song, Xueyuan Cui, Yi Wang

Accurate load forecasting serves as the foundation for the flexible operation of multi-energy systems (MES).

Data Valuation Load Forecasting

UrbanCLIP: Learning Text-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining from the Web

no code implementations22 Oct 2023 Yibo Yan, Haomin Wen, Siru Zhong, Wei Chen, Haodong Chen, Qingsong Wen, Roger Zimmermann, Yuxuan Liang

To answer the questions, we leverage the power of Large Language Models (LLMs) and introduce the first-ever LLM-enhanced framework that integrates the knowledge of textual modality into urban imagery profiling, named LLM-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining (UrbanCLIP).

Language Modelling Representation Learning

Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook

5 code implementations16 Oct 2023 Ming Jin, Qingsong Wen, Yuxuan Liang, Chaoli Zhang, Siqiao Xue, Xue Wang, James Zhang, Yi Wang, Haifeng Chen, XiaoLi Li, Shirui Pan, Vincent S. Tseng, Yu Zheng, Lei Chen, Hui Xiong

In this survey, we offer a comprehensive and up-to-date review of large models tailored (or adapted) for time series and spatio-temporal data, spanning four key facets: data types, model categories, model scopes, and application areas/tasks.

Time Series Time Series Analysis

OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling

1 code implementation NeurIPS 2023 Yi-Fan Zhang, Qingsong Wen, Xue Wang, Weiqi Chen, Liang Sun, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan

Online updating of time series forecasting models aims to address the concept drifting problem by efficiently updating forecasting models based on streaming data.

Time Series Time Series Forecasting

BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition

no code implementations28 Aug 2023 Shikai Fang, Qingsong Wen, Yingtao Luo, Shandian Zhe, Liang Sun

More importantly, almost all methods assume the observations are sampled at regular time stamps, and fail to handle complex irregular sampled time series arising from different applications.

Computational Efficiency Gaussian Processes +3

EasyTPP: Towards Open Benchmarking Temporal Point Processes

1 code implementation16 Jul 2023 Siqiao Xue, Xiaoming Shi, Zhixuan Chu, Yan Wang, Hongyan Hao, Fan Zhou, Caigao Jiang, Chen Pan, James Y. Zhang, Qingsong Wen, Jun Zhou, Hongyuan Mei

In this paper, we present EasyTPP, the first central repository of research assets (e. g., data, models, evaluation programs, documentations) in the area of event sequence modeling.

Benchmarking Point Processes

Benchmarks and Custom Package for Electrical Load Forecasting

1 code implementation14 Jul 2023 Zhixian Wang, Qingsong Wen, Chaoli Zhang, Liang Sun, Leandro Von Krannichfeldt, Yi Wang

Based on this, we conducted extensive experiments on load data at different levels, providing a reference for researchers to compare different load forecasting models.

Feature Engineering Load Forecasting +2

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection

1 code implementation7 Jul 2023 Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon, Cesare Alippi, Geoffrey I. Webb, Irwin King, Shirui Pan

In this survey, we provide a comprehensive review of graph neural networks for time series analysis (GNN4TS), encompassing four fundamental dimensions: forecasting, classification, anomaly detection, and imputation.

Anomaly Detection Imputation +2

DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection

2 code implementations17 Jun 2023 Yiyuan Yang, Chaoli Zhang, Tian Zhou, Qingsong Wen, Liang Sun

On the other hand, contrastive learning aims to find a representation that can clearly distinguish any instance from the others, which can bring a more natural and promising representation for time series anomaly detection.

Anomaly Detection Contrastive Learning +3

SaDI: A Self-adaptive Decomposed Interpretable Framework for Electric Load Forecasting under Extreme Events

no code implementations14 Jun 2023 Hengbo Liu, Ziqing Ma, Linxiao Yang, Tian Zhou, Rui Xia, Yi Wang, Qingsong Wen, Liang Sun

In this paper, we propose a novel forecasting framework, named Self-adaptive Decomposed Interpretable framework~(SaDI), which ensembles long-term trend, short-term trend, and period modelings to capture temporal characteristics in different components.

Load Forecasting Management

DiffLoad: Uncertainty Quantification in Load Forecasting with Diffusion Model

no code implementations31 May 2023 Zhixian Wang, Qingsong Wen, Chaoli Zhang, Liang Sun, Yi Wang

The uncertainties in load forecasting can be divided into two types: epistemic uncertainty and aleatoric uncertainty.

Decision Making energy management +3

CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting

1 code implementation20 May 2023 Wang Xue, Tian Zhou, Qingsong Wen, Jinyang Gao, Bolin Ding, Rong Jin

In this work, we design a special Transformer, i. e., Channel Aligned Robust Blend Transformer (CARD for short), that addresses key shortcomings of CI type Transformer in time series forecasting.

Time Series Time Series Forecasting

AHPA: Adaptive Horizontal Pod Autoscaling Systems on Alibaba Cloud Container Service for Kubernetes

no code implementations7 Mar 2023 Zhiqiang Zhou, Chaoli Zhang, Lingna Ma, Jing Gu, Huajie Qian, Qingsong Wen, Liang Sun, Peng Li, Zhimin Tang

This paper discusses horizontal POD resources management in Alibaba Cloud Container Services with a newly deployed AI algorithm framework named AHPA -- the adaptive horizontal pod auto-scaling system.


Robust Dominant Periodicity Detection for Time Series with Missing Data

no code implementations6 Mar 2023 Qingsong Wen, Linxiao Yang, Liang Sun

In this paper, we propose a robust and effective periodicity detection algorithm for time series with block missing data.

Time Series Time Series Analysis

DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models

1 code implementation31 Jan 2023 Haomin Wen, Youfang Lin, Yutong Xia, Huaiyu Wan, Qingsong Wen, Roger Zimmermann, Yuxuan Liang

Spatio-temporal graph neural networks (STGNN) have emerged as the dominant model for spatio-temporal graph (STG) forecasting.

Decision Making Denoising

AirFormer: Predicting Nationwide Air Quality in China with Transformers

1 code implementation29 Nov 2022 Yuxuan Liang, Yutong Xia, Songyu Ke, Yiwei Wang, Qingsong Wen, Junbo Zhang, Yu Zheng, Roger Zimmermann

Air pollution is a crucial issue affecting human health and livelihoods, as well as one of the barriers to economic and social growth.

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

1 code implementation24 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 +2

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).

Computational Efficiency feature selection +2

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

2 code implementations18 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 +1

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

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

From the perspective of network structure, we summarize the adaptations and modifications that have been made to Transformers in order to accommodate the challenges in time series analysis.

Anomaly Detection Time Series +1

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

3 code implementations30 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 Time Series Analysis

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 Cloud Computing +1

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 Time Series Forecasting +1

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 Clustering +3

RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series

1 code implementation5 Dec 2018 Qingsong Wen, Jingkun Gao, Xiaomin Song, Liang Sun, Huan Xu, Shenghuo Zhu

Based on the extracted trend, we apply the the non-local seasonal filtering to extract the seasonality component.

Anomaly Detection Time Series +1

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