Multivariate multi-step time series forecasting using sequence models (2/4)
Models - Option — 1 In this approach, the last time step of the encoder input is used as the <end> and <start> token for the encoder and the decoder respectively. Teacher forcing with shared weights In this model, we use teacher forcing to create an encoder-decoder model. The model architecture looks like -
Multivariate multi-step time series forecasting using sequence models (2/4) Models - Option — 1 In this approach, the last time step of the encoder input is used as the <end> and <start> token for the encoder and the decoder respectively. Teacher forcing with shared weights In this model, we use teacher forcing to create an encoder-decoder model. The model architecture looks like - nlp
Multivariate multistep time series forecasting using sequence model 24 Models Option — 1 approach last time step encoder input used end start token encoder decoder respectively Teacher forcing shared weight model use teacher forcing create encoderdecoder model model architecture look like nlp