mql5

Long-term forecasting of time series is a long-standing problem in solving various applied problems. Transformer-based models show promising results. However, high computational complexity and memory requirements make it difficult to use the Transformer for modeling long sequences. This has given rise to numerous studies devoted to reducing computational costs of the Transformer algorithm.
Despite the progress made by Transformer-based time series forecasting methods based, in some cases they fail to capture the common features of the time series distribution. The authors of the paper "FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting" have made an attempt to solve this problem. They compare the actual data of a time series with its predicted values obtained from the vanilla Transformer. Below is a screenshot from that paper.

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