When modeling time series, you will want a model that is sensitive both to short term and longer term movements. In other words, a Long Term Short Term Memory (LSTM).
Sepp Hochreiter invented this concept in his Master's thesis supervised by Jürgen Schmidhuber in Munich in the 1990s; today, it's the most-cited type of neural network.
LSTMs have been going the way of the dinosaurs since 2018. If you really need a complex neural network (over 1D convolution approaches), transformers are the current SOTA. Example implementation in "temporal fusion": https://pytorch-forecasting.readthedocs.io/en/stable/tutoria...
Mind you, in practice I've found these DL approaches overkill for simple problems of the "trends + cyclics + noise" kind.
My impression is that these kinds of models need a lot of data to train properly. I made a comment elsewhere in this thread, musing that tree ensemble models could do the same job, as a kind of low resolution quantized approximation. If you have experience in this area of research, I'd love to hear your thoughts on that.
When modeling time series, you will want a model that is sensitive both to short term and longer term movements. In other words, a Long Term Short Term Memory (LSTM).
Sepp Hochreiter invented this concept in his Master's thesis supervised by Jürgen Schmidhuber in Munich in the 1990s; today, it's the most-cited type of neural network.
Here are papers describing it: https://people.idsia.ch/~juergen/rnn.html
In Python, you can use TensorFlow's LSTMCell class: https://www.datacamp.com/tutorial/lstm-python-stock-market