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In this third notebook, I extend the hierarchical forecasting model from Part II by adding a neural network component to the state transition function. This creates a Hybrid Deep State-Space Model that combines probabilistic modeling with deep learning. 
 Why? This is a personal experiment to explore how to integrate neural networks with hierarchical models. It is not adding complexity for the…
In this third notebook, I extend the hierarchical forecasting model from Part II by adding a neural network component to the state transition function. This creates a Hybrid Deep State-Space Model that combines probabilistic modeling with deep learning.
Why? This is a personal experiment to explore how to integrate neural networks with hierarchical models. It is not adding complexity for the sake of complexity. It is rather an exploratory exercise to see if this approach can lead to better forecasting performance.
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