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I'm familiar with supervised learning algorithms like regression and neural networks which look at a bunch of input points and learn a function which outputs a value (the value varying depending on whether the algo is a classifier, logistic regression, or standard regression)..but I am facing a problem now where each observation I have is a couple of data points (TAQ data), and then after a fixed amount of these chronologically sorted events there is a spike and what appears to follow an exponential decay mean reversion. Every such instance described here is one of my observations and my goal is to predict/model this exponential decay mean reversion.

What type of algorithms would do something like this? time series models a-la ARCH/GARCH followed by a prediction look-ahead of N steps after training them or what else?

Thank you, any type of suggestions or advice/references are greatly appreciated.

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It's a very interesting question, but I think it belongs to statSE. – SRKX Nov 21 '11 at 11:25
you're probably right, ill take it over there – Palace Chan Nov 21 '11 at 21:03
You're working on the Kaggle competition, right? Try SVM on the whole series before the spike (so each observation is just a feature) – Dan Dec 3 '11 at 13:41

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