Author name cluster

Wei Cao

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

18 papers

2 author rows

IJCAI Conference 2025 Conference Paper

Deep Learning for Multivariate Time Series Imputation: A Survey

Jun Wang
Wenjie Du
Yiyuan Yang
Linglong Qian
Wei Cao
Keli Zhang
Wenjia Wang
Yuxuan Liang

Missing values are ubiquitous in multivariate time series (MTS) data, posing significant challenges for accurate analysis and downstream applications. In recent years, deep learning-based methods have successfully handled missing data by leveraging complex temporal dependencies and learned data distributions. In this survey, we provide a comprehensive summary of deep learning approaches for multivariate time series imputation (MTSI) tasks. We propose a novel taxonomy that categorizes existing methods based on two key perspectives: imputation uncertainty and neural network architecture. Furthermore, we summarize existing MTSI toolkits with a particular emphasis on the PyPOTS Ecosystem, which provides an integrated and standardized foundation for MTSI research. Finally, we discuss key challenges and future research directions, which give insight for further MTSI research. This survey aims to serve as a valuable resource for researchers and practitioners in the field of time series analysis and missing data imputation tasks. A well-maintained MTSI paper and tool list is available at https: //github. com/WenjieDu/Awesome_Imputation.

PDF Details DOI

AAAI Conference 2025 Conference Paper

HyperMixer: Specializable Hypergraph Channel Mixing for Long-term Multivariate Time Series Forecasting

Changyuan Tian
Zhicong Lu
Zequn Zhang
Heming Yang
Wei Cao
Zhi Guo
Xian Sun
Li Jin

Long-term Multivariate Time Series (LMTS) forecasting aims to predict extended future trends based on channel-interrelated historical data. Considering the elusive channel correlations, most existing methods compromise by treating channels as independent or tentatively modeling pairwise channel interactions, making it challenging to handle the characteristics of both higher-order interactions and time variation in channel correlations. In this paper, we propose HyperMixer, a novel specializable hypergraph channel mixing plugin which introduces versatile hypergraph structures to capture group channel interactions and time-varying patterns for long-term multivariate time series forecasting. Specifically, to encode the higher-order channel interactions, we structure multiple channels into a hypergraph, achieving a two-phase message-passing mechanism: channel-to-group and group-to-channel. Moreover, the functionally specializable hypergraph structures are presented to boost the capability of hypergraph to capture the time-varying patterns across periods, further refining modeling of channel correlations. Extensive experimental results on seven available benchmark datasets demonstrate the effectiveness and generalization of our plugin in LMTS forecasting. The visual analysis further illustrates that HyperMixer with specializable hypergraphs tailors channel interactions specific to certain periods.

PDF Details DOI

EAAI Journal 2024 Journal Article

Big data-assisted urban governance: A comprehensive system for business documents classification of the government hotline

Zicheng Zhang
Anguo Li
Li Wang
Wei Cao
Jianlin Yang

The government service platform, exemplified by the government hotline, has to handle extensive volumes of business documents that contain rich and timely public opinion information and citizens’ demands. However, manual processing struggles to process large-scale text data, adversely impacting operating costs and the quality of government services. This study proposes a comprehensive system for business document classification of the government hotline (BDCGHS) in China to address these challenges. BDCGHS leverages information entropy fused with term frequency-inverse document frequency (TF-IDF) weight to mine new words from business documents of the government hotline, and store them in a new word repository. These new words optimize Chinese word segmentation and text representation for text classification. We introduce a novel data structure called nested balanced binary tree to expedite new word mining, yielding a computational speed of almost five times than the Trie trees. Comparative experiments on the THUNews and government hotline datasets validate our proposed improvement BDCGHS algorithm’s superior performance 3 % over text classification algorithms. Compared to the latest bidirectional encoder representations from the transformers (BERT) model, BDCGHS enhances the accuracy of order dispatch based on business documents by almost 3 %. It has also demonstrated stable operations in two Chinese cities for over a year, yielding favorable results.

Details DOI

TMLR Journal 2024 Journal Article

NuTime: Numerically Multi-Scaled Embedding for Large- Scale Time-Series Pretraining

Chenguo Lin
Xumeng Wen
Wei Cao
Congrui Huang
Jiang Bian
Stephen Lin
Zhirong Wu

Recent research on time-series self-supervised models shows great promise in learning semantic representations. However, it has been limited to small-scale datasets, e.g., thousands of temporal sequences. In this work, we make key technical contributions that are tailored to the numerical properties of time-series data and allow the model to scale to large datasets, e.g., millions of temporal sequences. We adopt the Transformer architecture by first partitioning the input into non-overlapping windows. Each window is then characterized by its normalized shape and two scalar values denoting the mean and standard deviation within each window. To embed scalar values that may possess arbitrary numerical amplitudes to high-dimensional vectors, we propose a numerically multi-scaled embedding module enumerating all possible numerical scales for the scalars. The model undergoes pretraining with a simple contrastive objective on a large-scale dataset over a million sequences collected by merging existing public data. We study its transfer performance on a number of univariate and multivariate classification tasks, few shot learning, unsupervised clustering and anomaly detection benchmarks. Our method exhibits remarkable improvement against previous pretraining approaches and establishes the new state of the art, even compared with domain-specific non-learning-based methods.

PDF Details

JBHI Journal 2024 Journal Article

Removal of Ocular and Muscular Artifacts From Multi-Channel EEG Using Improved Spatial-Frequency Filtering

Wuxiang Shi
Yurong Li
Naiqing Cai
Rukai Chen
Wei Cao
Jixiang Li

Over recent decades, electroencephalogram (EEG) has become an essential tool in the field of clinical analysis and neurological disease research. However, EEG recordings are notably vulnerable to artifacts during acquisition, especially in clinical settings, which can significantly impede the accurate interpretation of neuronal activity. Blind source separation is currently the most popular method for EEG denoising, but most of the sources it separates often contain both artifacts and brain activity, which may lead to substantial information loss if handled improperly. In this paper, we introduce a dual-threshold denoising method combining spatial filtering with frequency-domain filtering to automatically eliminate electrooculogram (EOG) and electromyogram (EMG) artifacts from multi-channel EEG. The proposed method employs a fusion of second-order blind identification (SOBI) and canonical correlation analysis (CCA) to enhance source separation quality, followed by adaptive threshold to localize the artifact sources, and strict fixed threshold to remove strong artifact sources. Stationary wavelet transform (SWT) is utilized to decompose the weak artifact sources, with subsequent adjustment of wavelet coefficients in respective frequency bands tailored to the distinct characteristics of each artifact. The results of synthetic and real datasets show that our proposed method maximally retains the time-domain and frequency-domain information in the EEG during denoising. Compared with existing techniques, the proposed method achieves better denoising performance, which establishes a reliable foundation for subsequent clinical analyses.

Details DOI

ICML Conference 2023 Conference Paper

OpenFE: Automated Feature Generation with Expert-level Performance

Tianping Zhang
Zheyu Aqa Zhang
Zhiyuan Fan
Haoyan Luo
Fengyuan Liu
Qian Liu 0033
Wei Cao
Li Jian

The goal of automated feature generation is to liberate machine learning experts from the laborious task of manual feature generation, which is crucial for improving the learning performance of tabular data. The major challenge in automated feature generation is to efficiently and accurately identify effective features from a vast pool of candidate features. In this paper, we present OpenFE, an automated feature generation tool that provides competitive results against machine learning experts. OpenFE achieves high efficiency and accuracy with two components: 1) a novel feature boosting method for accurately evaluating the incremental performance of candidate features and 2) a two-stage pruning algorithm that performs feature pruning in a coarse-to-fine manner. Extensive experiments on ten benchmark datasets show that OpenFE outperforms existing baseline methods by a large margin. We further evaluate OpenFE in two Kaggle competitions with thousands of data science teams participating. In the two competitions, features generated by OpenFE with a simple baseline model can beat 99. 3% and 99. 6% data science teams respectively. In addition to the empirical results, we provide a theoretical perspective to show that feature generation can be beneficial in a simple yet representative setting.

Details

NeurIPS Conference 2022 Conference Paper

Efficient and Effective Multi-task Grouping via Meta Learning on Task Combinations

Xiaozhuang Song
Shun Zheng
Wei Cao
James Yu
Jiang Bian

As a longstanding learning paradigm, multi-task learning has been widely applied into a variety of machine learning applications. Nonetheless, identifying which tasks should be learned together is still a challenging fundamental problem because the possible task combinations grow exponentially with the number of tasks, and existing solutions heavily relying on heuristics may probably lead to ineffective groupings with severe performance degradation. To bridge this gap, we develop a systematic multi-task grouping framework with a new meta-learning problem on task combinations, which is to predict the per-task performance gains of multi-task learning over single-task learning for any combination. Our underlying assumption is that no matter how large the space of task combinations is, the relationships between task combinations and performance gains lie in some low-dimensional manifolds and thus can be learnable. Accordingly, we develop a neural meta learner, MTG-Net, to capture these relationships, and design an active learning strategy to progressively select meta-training samples. In this way, even with limited meta samples, MTG-Net holds the potential to produce reasonable gain estimations on arbitrary task combinations. Extensive experiments on diversified multi-task scenarios demonstrate the efficiency and effectiveness of our method. Specifically, in a large-scale evaluation with $27$ tasks, which produce over one hundred million task combinations, our method almost doubles the performance obtained by the existing best solution given roughly the same computational cost. Data and code are available at https: //github. com/ShawnKS/MTG-Net.

PDF Details

IS Journal 2020 Journal Article

A Deep Coupled LSTM Approach for USD/CNY Exchange Rate Forecasting

Wei Cao
Weidong Zhu
Wenjun Wang
Yves Demazeau
Chen Zhang

Forecasting CNY exchange rate accurately is a challenging task due to its complex coupling nature, which includes market-level coupling from interactions with multiple financial markets, macrolevel coupling from interactions with economic fundamentals, and deep coupling from interactions of the two aforementioned kinds of couplings. This study develops a new deep coupled long short-term memory (LSTM) approach, namely, DC-LSTM, to capture the complex couplings for USD/CNY exchange rate forecasting. In this approach, a deep structure consisting of stacked LSTMs is built to model the complex couplings. The experimental results with 10 years data indicate that the proposed approach significantly outperforms seven other benchmarks. The DC-LSTM is verified to be a useful tool to make wise investment decisions through a profitability discussion. The purpose in this article is to clarify the importance of coupling learning for exchange rate forecasting, and the usefulness of deep coupled model to capture the couplings.

Details DOI

NeurIPS Conference 2020 Conference Paper

MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler

Zhining Liu
Pengfei Wei
Jing Jiang
Wei Cao
Jiang Bian
Yi Chang

Imbalanced learning (IL), i. e. , learning unbiased models from class-imbalanced data, is a challenging problem. Typical IL methods including resampling and reweighting were designed based on some heuristic assumptions. They often suffer from unstable performance, poor applicability, and high computational cost in complex tasks where their assumptions do not hold. In this paper, we introduce a novel ensemble IL framework named MESA. It adaptively resamples the training set in iterations to get multiple classifiers and forms a cascade ensemble model. MESA directly learns the sampling strategy from data to optimize the final metric beyond following random heuristics. Moreover, unlike prevailing meta-learning-based IL solutions, we decouple the model-training and meta-training in MESA by independently train the meta-sampler over task-agnostic meta-data. This makes MESA generally applicable to most of the existing learning models and the meta-sampler can be efficiently applied to new tasks. Extensive experiments on both synthetic and real-world tasks demonstrate the effectiveness, robustness, and transferability of MESA. Our code is available at https: //github. com/ZhiningLiu1998/mesa.

PDF Details

AAAI Conference 2020 Conference Paper

Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting

Weiqi Chen
Ling Chen
Yu Xie
Wei Cao
Yusong Gao
Xiaojie Feng

Traffic forecasting is of great importance to transportation management and public safety, and very challenging due to the complicated spatial-temporal dependency and essential uncertainty brought about by the road network and traffic conditions. Latest studies mainly focus on modeling the spatial dependency by utilizing graph convolutional networks (GCNs) throughout a fixed weighted graph. However, edges, i. e. , the correlations between pair-wise nodes, are much more complicated and interact with each other. In this paper, we propose the Multi-Range Attentive Bicomponent GCN (MRA-BGCN), a novel deep learning model for traffic forecasting. We first build the node-wise graph according to the road network distance and the edge-wise graph according to various edge interaction patterns. Then, we implement the interactions of both nodes and edges using bicomponent graph convolution. The multi-range attention mechanism is introduced to aggregate information in different neighborhood ranges and automatically learn the importance of different ranges. Extensive experiments on two real-world road network traffic datasets, METR-LA and PEMS-BAY, show that our MRA-BGCN achieves the stateof-the-art results.

PDF Details

NeurIPS Conference 2018 Conference Paper

BRITS: Bidirectional Recurrent Imputation for Time Series

Wei Cao
Dong Wang
Jian Li
Hao Zhou
Lei Li
Yitan Li

Time series are widely used as signals in many classification/regression tasks. It is ubiquitous that time series contains many missing values. Given multiple correlated time series data, how to fill in missing values and to predict their class labels? Existing imputation methods often impose strong assumptions of the underlying data generating process, such as linear dynamics in the state space. In this paper, we propose BRITS, a novel method based on recurrent neural networks for missing value imputation in time series data. Our proposed method directly learns the missing values in a bidirectional recurrent dynamical system, without any specific assumption. The imputed values are treated as variables of RNN graph and can be effectively updated during the backpropagation. BRITS has three advantages: (a) it can handle multiple correlated missing values in time series; (b) it generalizes to time series with nonlinear dynamics underlying; (c) it provides a data-driven imputation procedure and applies to general settings with missing data. We evaluate our model on three real-world datasets, including an air quality dataset, a health-care data, and a localization data for human activity. Experiments show that our model outperforms the state-of-the-art methods in both imputation and classification/regression accuracies.

PDF Details

AAAI Conference 2018 Conference Paper

When Will You Arrive? Estimating Travel Time Based on Deep Neural Networks

Dong Wang
Junbo Zhang
Wei Cao
Jian Li
Yu Zheng

Estimating the travel time of any path (denoted by a sequence of connected road segments) in a city is of great importance to trafﬁc monitoring, route planning, ridesharing, taxi/Uber dispatching, etc. However, it is a very challenging problem, affected by diverse complex factors, including spatial correlations, temporal dependencies, external conditions (e. g. weather, trafﬁc lights). Prior work usually focuses on estimating the travel times of individual road segments or sub-paths and then summing up these times, which leads to an inaccurate estimation because such approaches do not consider road intersections/trafﬁc lights, and local errors may accumulate. To address these issues, we propose an end-to-end Deep learning framework for Travel Time Estimation (called DeepTTE) that estimates the travel time of the whole path directly. More speciﬁcally, we present a geo-convolution operation by integrating the geographic information into the classical convolution, capable of capturing spatial correlations. By stacking recurrent unit on the geo-convoluton layer, our DeepTTE can capture the temporal dependencies as well. A multi-task learning component is given on the top of DeepTTE, that learns to estimate the travel time of both the entire path and each local path simultaneously during the training phase. Extensive experiments on two trajectory datasets show our DeepTTE signiﬁcantly outperforms the state-of-the-art methods.

PDF Details

YNIMG Journal 2015 Journal Article

A method for estimating and removing streaking artifacts in quantitative susceptibility mapping

Wei Li
Nian Wang
Fang Yu
Hui Han
Wei Cao
Rebecca Romero
Bundhit Tantiwongkosi
Timothy Q. Duong

Quantitative susceptibility mapping (QSM) is a novel MRI method for quantifying tissue magnetic property. In the brain, it reflects the molecular composition and microstructure of the local tissue. However, susceptibility maps reconstructed from single-orientation data still suffer from streaking artifacts which obscure structural details and small lesions. We propose and have developed a general method for estimating streaking artifacts and subtracting them from susceptibility maps. Specifically, this method uses a sparse linear equation and least-squares (LSQR)-algorithm-based method to derive an initial estimation of magnetic susceptibility, a fast quantitative susceptibility mapping method to estimate the susceptibility boundaries, and an iterative approach to estimate the susceptibility artifact from ill-conditioned k-space regions only. With a fixed set of parameters for the initial susceptibility estimation and subsequent streaking artifact estimation and removal, the method provides an unbiased estimate of tissue susceptibility with negligible streaking artifacts, as compared to multi-orientation QSM reconstruction. This method allows for improved delineation of white matter lesions in patients with multiple sclerosis and small structures of the human brain with excellent anatomical details. The proposed methodology can be extended to other existing QSM algorithms.

Details DOI

AAAI Conference 2015 Conference Paper

Deep Modeling Complex Couplings within Financial Markets

Wei Cao
Liang Hu
Longbing Cao

The global financial crisis occurred in 2008 and its contagion to other regions, as well as the long-lasting impact on different markets, show that it is increasingly important to understand the complicated coupling relationships across financial markets. This is indeed very difficult as complex hidden coupling relationships exist between different financial markets in various countries, which are very hard to model. The couplings involve interactions between homogeneous markets from various countries (we call intra-market coupling), interactions between heterogeneous markets (inter-market coupling) and interactions between current and past market behaviors (temporal coupling). Very limited work has been done towards modeling such complex couplings, whereas some existing methods predict market movement by simply aggregating indicators from various markets but ignoring the inbuilt couplings. As a result, these methods are highly sensitive to observations, and may often fail when financial indicators change slightly. In this paper, a coupled deep belief network is designed to accommodate the above three types of couplings across financial markets. With a deep-architecture model to capture the high-level coupled features, the proposed approach can infer market trends. Experimental results on data of stock and currency markets from three countries show that our approach outperforms other baselines, from both technical and business perspectives.

PDF Details

IS Journal 2015 Journal Article

Financial Crisis Forecasting via Coupled Market State Analysis

Wei Cao
Longbing Cao

Financial crisis forecasting has been a long-standing challenge that often involves couplings between indicators of multiple markets. Such couplings include implicit relations that might not be effectively detected from raw market observations. However, most methods for crisis forecasting rely directly on market observations and might not detect the hidden interactions between markets. To this end, the authors explore coupled market state analysis (CMSA), assuming that the observations of markets are governed by a collection of intra- and intercoupled hidden market states. Accordingly, they built a forecaster based on these coupled market states instead of observations.

Details DOI

NeurIPS Conference 2015 Conference Paper

On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs

Wei Cao
Jian Li
Yufei Tao
Zhize Li

This paper discusses how to efficiently choose from $n$ unknowndistributions the $k$ ones whose means are the greatest by a certainmetric, up to a small relative error. We study the topic under twostandard settings---multi-armed bandits and hidden bipartitegraphs---which differ in the nature of the input distributions. In theformer setting, each distribution can be sampled (in the i. i. d. manner) an arbitrary number of times, whereas in the latter, eachdistribution is defined on a population of a finite size $m$ (andhence, is fully revealed after $m$ samples). For both settings, weprove lower bounds on the total number of samples needed, and proposeoptimal algorithms whose sample complexities match those lower bounds.

PDF Details

AAAI Conference 2014 Conference Paper

Deep Modeling of Group Preferences for Group-Based Recommendation

Liang Hu
Jian Cao
Guandong Xu
Longbing Cao
Zhiping Gu
Wei Cao

Nowadays, most recommender systems (RSs) mainly aim to suggest appropriate items for individuals. Due to the social nature of human beings, group activities have become an integral part of our daily life, thus motivating the study on group RS (GRS). However, most existing methods used by GRS make recommendations through aggregating individual ratings or individual predictive results rather than considering the collective features that govern user choices made within a group. As a result, such methods are heavily sensitive to data, hence they often fail to learn group preferences when the data are slightly inconsistent with predefined aggregation assumptions. To this end, we devise a novel GRS approach which accommodates both individual choices and group decisions in a joint model. More specifically, we propose a deep-architecture model built with collective deep belief networks and dual-wing restricted Boltzmann machines. With such a deep model, we can use high-level features, which are induced from lower-level features, to represent group preference so as to relieve the vulnerability of data. Finally, the experiments conducted on a real-world dataset prove the superiority of our deep model over other state-of-the-art methods.

PDF Details

YNIMG Journal 2014 Journal Article

Prenatal alcohol exposure reduces magnetic susceptibility contrast and anisotropy in the white matter of mouse brains

Wei Cao
Wei Li
Hui Han
Shonagh K. O'Leary-Moore
Kathleen K. Sulik
G. Allan Johnson
Chunlei Liu

Prenatal alcohol exposure can result in long-term cognitive and behavioral deficits. Fetal alcohol spectrum disorder (FASD) refers to a range of permanent birth defects caused by prenatal alcohol exposure, and is the most common neurodevelopmental disorder in the US. Studies by autopsy and conventional structural MRI indicate that the midline structures of the brain are particularly vulnerable to prenatal alcohol exposure. Diffusion tensor imaging (DTI) has shown that abnormalities in brain white matter especially the corpus callosum are very common in FASD. Quantitative susceptibility mapping (QSM) is a novel technique that measures tissue's magnetic property. Such magnetic property is affected by tissue microstructure and molecular composition including that of myelin in the white matter. In this work, we studied three major white matter fiber bundles of a mouse model of FASD and compared it to control mice using both QSM and DTI. QSM revealed clear and significant abnormalities in anterior commissure, corpus callosum, and hippocampal commissure, which were likely due to reduced myelination. Our data also suggested that QSM may be even more sensitive than DTI for examining changes due to prenatal alcohol exposure. Although this is a preclinical study, the technique of QSM is readily translatable to human brain.

Details DOI