  Time series forecasting is widely used in extensive applications, such as
traffic planning and weather forecasting. However, real-world time series
usually present intricate temporal variations, making forecasting extremely
challenging. Going beyond the mainstream paradigms of plain decomposition and
multiperiodicity analysis, we analyze temporal variations in a novel view of
multiscale-mixing, which is based on an intuitive but important observation
that time series present distinct patterns in different sampling scales. The
microscopic and the macroscopic information are reflected in fine and coarse
scales respectively, and thereby complex variations can be inherently
disentangled. Based on this observation, we propose TimeMixer as a fully
MLP-based architecture with Past-Decomposable-Mixing (PDM) and
Future-Multipredictor-Mixing (FMM) blocks to take full advantage of
disentangled multiscale series in both past extraction and future prediction
phases. Concretely, PDM applies the decomposition to multiscale series and
further mixes the decomposed seasonal and trend components in fine-to-coarse
and coarse-to-fine directions separately, which successively aggregates the
microscopic seasonal and macroscopic trend information. FMM further ensembles
multiple predictors to utilize complementary forecasting capabilities in
multiscale observations. Consequently, TimeMixer is able to achieve consistent
state-of-the-art performances in both long-term and short-term forecasting
tasks with favorable run-time efficiency.
