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I somehow find the concept of a general time series model strange. How can the same model predict egg prices in Italy, and global inflation in a reliable way? And how would you even use this model, given that there are no explanations that help you trust where the prediction comes from…
Earlier quoted context omitted.
Personally, coming from an EE background and not finance or statistics, I would go about identifying these patterns with an Signals & Systems toolbox, like systems identification, various matched filters/classifiers. This might be a totall wrong approach, but I think it might make sense to try to model a matched filter based on previous stock selloff/bullrun trigger events, and then see if the it has any predictive a…
You're crafting features. The modern approach to ML (deep learning) is to use over-parameterized models and let them learn the features. Perhaps you remember this? https://www.nytimes.com/2012/06/26/technology/in-a-big-netwo...
I somehow find the concept of a general time series model strange. How can the same model predict egg prices in Italy, and global inflation in a reliable way? And how would you even use this model, given that there are no explanations that help you trust where the prediction comes from…
What is not generally understood is that these models don’t predict egg prices or inflation in Italy. They decompose a time series into trends, seasonality and residuals. That’s what they are actually modelling. They cannot predict wars in the Middle East influencing inflation unless there is a seasonal pattern(s).
I somehow find the concept of a general time series model strange. How can the same model predict egg prices in Italy, and global inflation in a reliable way? And how would you even use this model, given that there are no explanations that help you trust where the prediction comes from…
Earlier quoted context omitted.
You're crafting features. The modern approach to ML (deep learning) is to use over-parameterized models and let them learn the features. Perhaps you remember this? https://www.nytimes.com/2012/06/26/technology/in-a-big-netwo...
Except that their success in the time series domain has been rather lackluster and elusive. It will s one of the few domains where old school models are not only less work to maintain but also more accurate. There are a few exceptions here and there. Every year there are a few neural nets based challengers. You can follow the M series of computations from its start to see this evolution.
Earlier quoted context omitted.
You're crafting features. The modern approach to ML (deep learning) is to use over-parameterized models and let them learn the features. Perhaps you remember this? https://www.nytimes.com/2012/06/26/technology/in-a-big-netwo...
Except that their success in the time series domain has been rather lackluster and elusive. It will s one of the few domains where old school models are not only less work to maintain but also more accurate. There are a few exceptions here and there. Every year there are a few neural nets based challengers. You can follow the M series of computations from its start to see this evolution.
Let me be blunt: Shannon would tell us that time forecasting is bullshit: There is infinitely more entropy in the real world out there than any model can even remotely capture. The world is not minecraft.
for a model to be useful, it doesnt need to capture the behavior of a system. It only needs to capture signals which can be useful. For example, for a biased coin toss, a model is already useful if it can predict a little better than random.
Earlier quoted context omitted.
Except that their success in the time series domain has been rather lackluster and elusive. It will s one of the few domains where old school models are not only less work to maintain but also more accurate. There are a few exceptions here and there. Every year there are a few neural nets based challengers. You can follow the M series of computations from its start to see this evolution.
LightGBM won M5 and it wasn't even a competition.
The M series of competitions change the tasks every year to explore what models perform best under different scenarios. As I mentioned, neural network based models win here and there, but very spotty performance over all.
Earlier quoted context omitted.
Except that their success in the time series domain has been rather lackluster and elusive. It will s one of the few domains where old school models are not only less work to maintain but also more accurate. There are a few exceptions here and there. Every year there are a few neural nets based challengers. You can follow the M series of computations from its start to see this evolution.
Maybe because useful time-series modeling is usually really about causal modeling? My understanding is that mediated causality in particular is still very difficult, where adding extra hops in the middle takes CoT performance from like 90% to 10%.
NNs do ok on those time series problems where it is really about learning a function directly off time. This is nonlinear regression where time is just another input variable.
Cases where one has to adjust for temporaly correlated errors, those seem to be harder for NNs. BTW I am talking about accuracies beyond what a typical RNN variants will achieve, which is pretty respectable. It's the case that more complicated DNNs don't seem to do much better inspite of their significant model complexity.