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Xiaoye Miao

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5 papers

1 author row

IJCAI Conference 2025 Conference Paper

General Incomplete Time Series Analysis via Patch Dropping Without Imputation

Yangyang Wu
Yi Yuan
Mengying Zhu
Xiaoye Miao
Meng Xi

Missing values in multivariate time series data present significant challenges to effective analysis. Existing methods for multivariate time series analysis either ignore missing data, sacrificing performance, or follow the impute-then-analyze paradigm, which suffers from redundant training and error accumulation, leading to biased results and suboptimal performance. In this paper, we propose INTER, a novel end-to-end framework for incomplete multivariate time series analysis, which bypasses imputation by leveraging pre-trained language models to learn the distribution of incomplete time series data. INTER incorporates two novel components: the missing-rate-aware time series patch-dropping (MPD) strategy and the missing-aware Transformer block, both of which we propose to enhance model generalization, robustness, and the ability to capture underlying patterns in the observed incomplete time series. Moreover, we theoretically prove that the MPD strategy exhibits lower sample variance for time series with the same dropout rate compared to other dropping strategies. Extensive experiments on 11 public real-world time series datasets demonstrate that INTER improves accuracy by over 20% compared to state-of-the-art methods, while maintaining competitive computational efficiency.

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IJCAI Conference 2025 Conference Paper

MMNet: Missing-Aware and Memory-Enhanced Network for Multivariate Time Series Imputation

Xiaoye Miao
Han Shi
Yi Yuan
Daozhan Pan
Yangyang Wu
Xiaohua Pan

Multivariate time series (MTS) data in real-world scenarios are often incomplete, which hinders effective data analysis. Therefore, MTS imputation has been widely studied to facilitate various MTS tasks. Existing imputation methods primarily initialize missing values with zeros in order to perform effective incomplete MTS encoding, which impede the model's capacity to precisely discern the missing distribution. Moreover, these methods often overlook the global similarity in time series but are limited in the use of local information within the sample. To this end, we propose a novel multivariate time series imputation network model, named MMNet. MMNet introduces a Missing-Aware Embedding (MAE) approach to adaptively represent incomplete MTS, allowing the model to better distinguish between missing and observed data. Furthermore, we design a Memory-Enhanced Encoder (MEE) aimed at modeling prior knowledge through memory mechanism, enabling better utilization of the global similarity within the time series. Building upon this, MMNet incorporates a Multi-scale Mixing architecture (MSM) that leverages information from multiple scales to enhance the final imputation. Extensive experiments on four public real-world datasets demonstrate that, MMNet yields a more than 25% gain in performance, compared with the state-of-the-art methods.

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I&C Journal 2024 Journal Article

A truthful near-optimal mechanism for online linear packing-covering problem in the random order model

Jinshan Zhang
Xiaoye Miao
Meng Xi
Tianyu Du
Jianwei Yin

Our focus is on the online linear packing-covering problem (OLPCP). Within this domain, we present an algorithm that attains near-optimal performance based on generalized Chernoff bounds for general random variables, assuming inputs are received in a uniformly random order and under almost stringent conditions. Through VCG protocols, we are able to unveil the inaugural truthful near-optimal mechanism for OLPCP, all within the confines of nearly tight conditions.

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AAAI Conference 2023 Conference Paper

Jointly Imputing Multi-View Data with Optimal Transport

Yangyang Wu
Xiaoye Miao
Xinyu Huang
Jianwei Yin

The multi-view data with incomplete information hinder the effective data analysis. Existing multi-view imputation methods that learn the mapping between complete view and completely missing view are not able to deal with the common multi-view data with missing feature information. In this paper, we propose a generative imputation model named Git with optimal transport theory to jointly impute the missing features/values, conditional on all observed values from the multi-view data. Git consists of two modules, i.e., a multi-view joint generator (MJG) and a masking energy discriminator (MED). The generator MJG incorporates a joint autoencoder with the multiple imputation rule to learn the data distribution from all observed multi-view data. The discriminator MED leverages a new masking energy divergence function to make Git differentiable for imputation enhancement. Extensive experiments on several real-world multi-view data sets demonstrate that, Git yields over 35% accuracy gain, compared to the state-of-the-art approaches.

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AAAI Conference 2021 Conference Paper

Generative Semi-supervised Learning for Multivariate Time Series Imputation

Xiaoye Miao
Yangyang Wu
Jun Wang
Yunjun Gao
Xudong Mao
Jianwei Yin

The missing values, widely existed in multivariate time series data, hinder the effective data analysis. Existing time series imputation methods do not make full use of the label information in real-life time series data. In this paper, we propose a novel semi-supervised generative adversarial network model, named SSGAN, for missing value imputation in multivariate time series data. It consists of three players, i. e. , a generator, a discriminator, and a classifier. The classifier predicts labels of time series data, and thus it drives the generator to estimate the missing values (or components), conditioned on observed components and data labels at the same time. We introduce a temporal reminder matrix to help the discriminator better distinguish the observed components from the imputed ones. Moreover, we theoretically prove that, SSGAN using the temporal reminder matrix and the classifier does learn to estimate missing values converging to the true data distribution when the Nash equilibrium is achieved. Extensive experiments on three public real-world datasets demonstrate that, SSGAN yields a more than 15% gain in performance, compared with the state-of-the-art methods.

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