Advanced Deep Learning Architectures for Time Series Forecasting
In previous chapters, we’ve learned how to create forecasting models using different types of neural networks but, so far, we’ve worked with basic architectures such as feedforward neural networks or LSTMs. This chapter describes how to build forecasting models with state-of-the-art approaches such as DeepAR or Temporal Fusion Transformers. These have been developed by tech giants such as Google and Amazon and are available in different Python libraries. These advanced deep learning architectures are designed to tackle different types of forecasting problems.
We’ll cover the following recipes:
- Interpretable forecasting with N-BEATS
- Optimizing the learning rate with PyTorch Forecasting
- Getting started with GluonTS
- Training a DeepAR model with GluonTS
- Training a Transformer with NeuralForecast
- Training a Temporal Fusion Transformer with GluonTS
- Training an Informer...
