Taking Neural Networks to the next level - page 38

[Sergey Golubev]

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Sergey Golubev, 2024.09.22 07:41

Neural Networks Made Easy (Part 88): Time-Series Dense Encoder (TiDE)

Probably all known neural network architectures have been studied in terms of their ability to solve time series forecasting problems, including recurrent, convolutional and graph models. The most notable results are demonstrated by models based on the Transformer architecture. Several such algorithms were also presented in this series of articles. However, recent research has shown that Transformer-based architectures might be less powerful than expected. On some time series forecasting benchmarks, simple linear models can show comparable or even better performance. Unfortunately, however, such linear models have shortcomings because they are not suitable for modeling nonlinear relationships between a sequence of time series and time-independent covariates.