Corpus ID: 222142244

A Transformer-based Framework for Multivariate Time Series Representation Learning

@article{Zerveas2020ATF,
  title={A Transformer-based Framework for Multivariate Time Series Representation Learning},
  author={G. Zerveas and Srideepika Jayaraman and Dhaval Patel and A. Bhamidipaty and Carsten Eickhoff},
  journal={ArXiv},
  year={2020},
  volume={abs/2010.02803}
}
In this work we propose for the first time a transformer-based framework for unsupervised representation learning of multivariate time series. Pre-trained models can be potentially used for downstream tasks such as regression and classification, forecasting and missing value imputation. By evaluating our models on several benchmark datasets for multivariate time series regression and classification, we show that not only does our modeling approach represent the most successful method employing… Expand
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References

SHOWING 1-10 OF 38 REFERENCES
Unsupervised Scalable Representation Learning for Multivariate Time Series
Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case
A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data
Deep learning for time series classification: a review
InceptionTime: Finding AlexNet for Time Series Classification
Similarity Preserving Representation Learning for Time Series Analysis
ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels
...
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