title: A Transformer-based Framework for Multivariate Time Series Representation Learning
abstract: 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. We evaluate our models on several benchmark datasets for multivariate time series regression and classification and show that they exceed current state-of-the-art performance, even when the number of training samples is very limited, while at the same time offering computational efficiency. We show that unsupervised pre-training of our transformer models offers a substantial performance benefit over fully supervised learning, even without leveraging additional unlabeled data, i.e., by reusing the same data samples through the unsupervised objective.
authors: George Zerveas Srideepika Jayaraman Dhaval Patel Anuradha Bhamidipaty Carsten Eickhoff
hIndex: Anuradha Bhamidipaty 8