Aug. 10, 2023, 4:44 a.m. | Abhimanyu Das, Weihao Kong, Andrew Leach, Shaan Mathur, Rajat Sen, Rose Yu

cs.LG updates on arXiv.org arxiv.org

Recent work has shown that simple linear models can outperform several
Transformer based approaches in long term time-series forecasting. Motivated by
this, we propose a Multi-layer Perceptron (MLP) based encoder-decoder model,
Time-series Dense Encoder (TiDE), for long-term time-series forecasting that
enjoys the simplicity and speed of linear models while also being able to
handle covariates and non-linear dependencies. Theoretically, we prove that the
simplest linear analogue of our model can achieve near optimal error rate for
linear dynamical systems (LDS) …

arxiv decoder encoder encoder-decoder forecasting linear long-term mlp perceptron series simple simplicity speed transformer work

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