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Old 08-25-2012, 05:38 AM
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magdon magdon is offline
Join Date: Aug 2009
Location: Troy, NY, USA.
Posts: 595
Default Re: Recency weighted regression

Yes, that would be a way to run the process and estimate how good the predictor is.

Originally Posted by itooam View Post
Thanks again for the thorough response Dr Magdon. I think we are talking along the same lines just a bit is lost in translation - one of the disadvantages of written communication. I apologies for my wording though, I don't mean to confuse; I used the words "Recency weighted regression" without knowing that this generally means something else in the machine learning literature.

I also think I now understand more clearly the application of
E_{in}=\sum_{t>3}\alpha_t(\mathbf{w\cdot x_t}-y_t)^2 so thanks again for explaining. I think I need to read up on this more as this makes me question: "how do I measure how well this recency weighting would have performed in the past?". I assume to answer this you would need to loop through the above formula starting from an arbitrary start date i.e., starting with a dataset equal to the rule of thumb: 10 x DegreesOfFreedom e.g., in context of the simplest model (\mathbf{x}_t=[1,P_{t-1},P_{t-2},P_{t-3},P_{t-4}]) we would start with a dataset of the first 50 days... pseudocode:

for i=50 to 996 step 1
.......\mathbf{x} = wholeDataSet[items 1 to i] the regression on \mathbf{x} and find \mathbf{w} by minimising E_{in}=\sum_{t=1}^{i}\alpha_t(\mathbf{w\cdot x_t}-y_t)^2
.......error = error + (\mathbf{w\cdot x_{(i+1)}}-y_{(i+1)})^2
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