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Multivariate Time Series Imputation With Transformers
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AI
DOI：10.1109/LSP.2022.3224880.png)
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摘要
En 中文
Processing time series with missing segments is a fundamental challenge that puts obstacles to advanced analysis in various disciplines such as engineering, medicine, and economics. One of the remedies is imputation to fill the missing values based on observed values properly without undermining performance. We propose the Multivariate Time-Series Imputation with Transformers (MTSIT), a novel method that uses transformer architecture in an unsupervised manner for missing value imputation. Unlike the existing transformer architectures, this model only uses the encoder part of the transformer due to computational benefits. Crucially, MTSIT trains the autoencoder by jointly reconstructing and imputing stochastically-masked inputs via an objective designed for multivariate time-series data. The trained autoencoder is then evaluated for imputing both simulated and real missing values. Experiments show that MTSIT outperforms state-of-the-art imputation methods over benchmark datasets.
Keyword:
Transformers
Time series analysis
Training
Decoding
Data models
Medical services
Computational modeling
Deep learning
imputation
multivariate time series
time series
transformer
unsupervised learning
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
机构
引用论文
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