Discover the structure of TiDE and apply it in a forecasting mission utilizing Python

In our exploration of the most recent advances within the subject of time collection forecasting, we found N-HiTS, PatchTST, TimeGPT and likewise TSMixer.

Whereas many efforts have been deployed to use the Transformer structure for forecasting, it seems that it achieves a mediocre efficiency contemplating the computation necessities.

In reality, easy linear fashions have been proven to outperform the complicated Transformer-based fashions on many benchmark datasets (see Zheng et al., 2022).

Motivated by that, in April 2023, researchers at Google proposed TiDE: a long-term forecasting mannequin with an encoder-decoder structure constructed with Multilayer Perceptrons (MLPs).

Of their paper Lengthy-term Forecasting with TiDE: Time-series Dense Encoder, the authors display that the mannequin achieves state-of-the-art outcomes on quite a few datasets when in comparison with different Transformer-based and MLP-based fashions, like PatchTST and N-HiTS respectively.

On this article, we first discover the structure and inside workings of TiDE. Then, we apply the mannequin in Python and use it in our personal small forecasting experiment.

For extra particulars on TiDE, be certain that to learn the unique paper.

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Let’s get began!

TiDE stands for Time-series Dense Encoder. At its base, this mannequin implements the encoder-decoder idea with out the eye mechanism in Transformer-based fashions.

As a substitute, it depends on MLPs to attain sooner coaching and inference occasions, whereas attaining good efficiency.

Throughout coaching, the mannequin will encode historic knowledge together with covariates. Then, it can decode the discovered illustration together with recognized future covariates…