  Large-scale self-supervised pre-training Transformer architecture have
significantly boosted the performance for various tasks in natural language
processing (NLP) and computer vision (CV). However, there is a lack of
researches on processing multivariate time-series by pre-trained Transformer,
and especially, current study on masking time-series for self-supervised
learning is still a gap. Different from language and image processing, the
information density of time-series increases the difficulty of research. The
challenge goes further with the invalidity of the previous patch embedding and
mask methods. In this paper, according to the data characteristics of
multivariate time-series, a patch embedding method is proposed, and we present
an self-supervised pre-training approach based on Masked Autoencoders (MAE),
called MTSMAE, which can improve the performance significantly over supervised
learning without pre-training. Evaluating our method on several common
multivariate time-series datasets from different fields and with different
characteristics, experiment results demonstrate that the performance of our
method is significantly better than the best method currently available.
