Sequence Complementor: Complementing Transformers For Time Series
Forecasting with Learnable Sequences
Sequence Complementor: Complementing Transformers For Time Series
Forecasting with Learnable Sequences
Since its introduction, the transformer has shifted the development trajectory away from traditional models (e.g., RNN, MLP) in time series forecasting, which is attributed to its ability to capture global dependencies within temporal tokens. Follow-up studies have largely involved altering the tokenization and self-attention modules to better adapt Transformers for …