Uncover and perceive the inside workings of TimeMixer and apply it in your personal forecasting mission utilizing Python
The sector of time collection forecasting retains evolving at a speedy tempo, with many fashions being proposed and claiming state-of-the-art efficiency.
Deep studying fashions at the moment are widespread strategies for time collection forecasting, particularly on giant datasets with many options.
Though quite a few fashions have been proposed lately, such because the iTransformer, SOFTS, and TimesNet, their efficiency typically falls quick in different benchmarks towards fashions like NHITS, PatchTST and TSMixer.
In Could 2024, a brand new mannequin was proposed: TimeMixer. Based on the unique paper, TimeMixer: Decomposable Multiscale Mixing for Time Collection Forecasting, this mannequin makes use of mixing of options together with collection decomposition in an MLP-based structure to supply forecasts.
On this article, we first discover the inside workings of TimeMixer earlier than working our personal little benchmark in each quick and lengthy horizon forecasting duties.
As all the time, be sure that to learn the unique analysis article for extra particulars.
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