  Multivariate time series forecasting has seen widely ranging applications in
various domains, including finance, traffic, energy, and healthcare. To capture
the sophisticated temporal patterns, plenty of research studies designed
complex neural network architectures based on many variants of RNNs, GNNs, and
Transformers. However, complex models are often computationally expensive and
thus face a severe challenge in training and inference efficiency when applied
to large-scale real-world datasets. In this paper, we introduce LightTS, a
light deep learning architecture merely based on simple MLP-based structures.
The key idea of LightTS is to apply an MLP-based structure on top of two
delicate down-sampling strategies, including interval sampling and continuous
sampling, inspired by a crucial fact that down-sampling time series often
preserves the majority of its information. We conduct extensive experiments on
eight widely used benchmark datasets. Compared with the existing
state-of-the-art methods, LightTS demonstrates better performance on five of
them and comparable performance on the rest. Moreover, LightTS is highly
efficient. It uses less than 5% FLOPS compared with previous SOTA methods on
the largest benchmark dataset. In addition, LightTS is robust and has a much
smaller variance in forecasting accuracy than previous SOTA methods in long
sequence forecasting tasks.
