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Former Reuters Research Director here. When modeling time series, you will want a model that is sensitive both to short term and longer term movements. In othe
by jll29 4y ago
Former Reuters Research Director here.
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 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 https://www.datacamp.com/tutorial/lstm-python-stock-market
- sabertoothed 4y agoI don't think LSTMs are state-of-the-art in the domain anymore.
- Tarq0n 4y agoLSTMs never did beat well tuned statistical models anyway. Neural nets are only taking over forecasting in the last few years.
- isoprophlex 4y agoLSTMs 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/tutorials/stallion.html 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.
- nerdponx 4y agoMy 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.