Specialized Deep Learning Architectures for Forecasting
Our journey through the world of deep learning (DL) is coming to an end. In the previous chapter, we were introduced to the global paradigm of forecasting and saw how we can make a simple model such as a Recurrent Neural Network (RNN) perform close to the high benchmark set by global machine learning models. In this chapter, we are going to review a few popular DL architectures that were designed specifically for time series forecasting. With these more sophisticated model architectures, we will be better equipped at handling problems in the wild that call for more powerful models than vanilla RNNs and LSTMs.
In this chapter, we will be covering these main topics:
- The need for specialized architectures
- Neural Basis Expansion Analysis for Interpretable Time Series Forecasting (N-BEATS)
- Neural Basis Expansion Analysis for Interpretable Time Series Forecasting with Exogenous Variables (N-BEATSx)
- Neural Hierarchical...
