Encoder-Decoder network in PyTorch for seq2seq time series forecasting using scheduled sampling

Time series forecasting is a common problem in machine learning where the goal is to predict future values based on past observations. One popular approach to time series forecasting is using sequence-to-sequence (seq2seq) models, which use encoder-decoder architectures to learn the relationship between input sequences and target sequences.

In this article, we will focus on implementing an Encoder-Decoder network in PyTorch for time series forecasting. We will also introduce the concept of scheduled sampling, which is a technique used to combat the exposure bias problem in seq2seq models.

Encoder-Decoder network

The Encoder-Decoder network consists of two main components: an encoder and a decoder. The encoder takes in the input sequence and processes it into a fixed-size context vector, which captures the important information from the input sequence. The decoder then uses this context vector to generate the output sequence one timestep at a time.

Implementing an Encoder-Decoder network in PyTorch involves creating separate modules for the encoder and decoder, and then combining them into a single model. The encoder typically consists of recurrent neural network (RNN) layers such as LSTM or GRU, while the decoder can also be implemented using RNN layers.

Scheduled sampling

Scheduled sampling is a technique used to address the exposure bias problem in seq2seq models. Exposure bias refers to the mismatch between the training and inference phases, where the model is always fed with ground truth inputs during training but needs to generate its own inputs during inference.

To overcome exposure bias, scheduled sampling gradually introduces the model to its own predictions during training. Instead of always using ground truth inputs, the model is fed with either the ground truth or its own predictions based on a sampling probability schedule. This helps the model learn to make accurate predictions even in the absence of ground truth inputs.

Conclusion

In this article, we have discussed the Encoder-Decoder network in PyTorch for time series forecasting and introduced the concept of scheduled sampling. By implementing scheduled sampling in our seq2seq model, we can improve the accuracy of our predictions and make our model more robust to exposure bias.