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Old 01-18-2016, 01:45 PM
ksokhanvari ksokhanvari is offline
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Join Date: Jan 2016
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Default Time Series method similarities

Dear Professor Abu-Mostafa

First I like to add my thanks and appreciation to countless other messages that you surely have received about this wonderful course. I am 53 years old and although I focused on AI techniques during my Master’s Degree in computer science, when I was younger , I did not have the quality of understanding I have gained after completing this course. AI has come a long way in 25 years and I am very excited to have discovered this class online. Congratulation to you and the Caltech community for this high quality work.

I do have a question regarding an application area regarding financial market forecasting. I have been working in this area for the past 10 years applying the typical methods of time series analysis to the problem of forecasting time series quantities. It seems to me that while time series analysis in the literature is covered as a separate field entirely, the application of ARIMA and GARCH models and the parameters fitting procedures found in the literature and software libraries share a significant amount of theoretical overlap to machine learning theory.

Could you please comment on how would you map these techniques (similarities and differences) to the machine learning map you presented? In particular, it seems the data handling and validation procedures should be the same. The ARIMA and GARCH models are just another hypothesis set model. The fitting procedures are similar to learning algos. The minimum AIC or Maximum likelihood model selection procedures is similar to regularization and VC dimension analysis, Occam razor etc., etc.

Additionally, in your experience given that today machine learning techniques are often applied to financial forecasting domain -- have you found a typical algorithm, i.e. NNs, SVMs, etc. have typically a better performance in this domain? the evidence in the literature is not clear to me.

I promise to charge the appropriate VC dimension cost to my solution sets !


Thanks again,

Regards,

Kamran Sokhanvari
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arima, garch, time series

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