Code: https://github.com/nklingen/Transformer-Time-Series-Forecasting

This article will present a Transformer-decoder architecture for forecasting on a humidity time-series data-set provided by Woodsense . This project is a follow-up on a previous project that involved training an LSTM on the same data-set. The LSTM was seen to suffer from “short-term memory” over long sequences. Consequently, a Transformer will be used in this project, which outperforms the LSTM on the same data-set.

Inspired by the graphic in D2L¹

Why use a Transformer ?

LSTMs process tokens sequentially, as shown above. This architecture maintains a hidden state that is updated with every new input token, representing the entire sequence it has seen. Theoretically, very important information can…

Natasha Klingenbrunn

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