Transformers, Deep Learning, Time Series Analysis, Forecasting, Machine Learning.
different encoding strategies by removing positional and temporal encoding from the transformers. These results were mixed depending on the model. For FEDFormer [6] and Autoformer [4], removing positional encoding improved the performance on the Traffic dataset on most look-back window sizes.