#1




Recency weighted regression
Hi,
I wondered if anyone could help with the following: (I'll make up a fictional example to explain in simple terms what I am trying to do): If for example you created an extremely simple model that was to predict whether a share price was to rise or fall (for now we'll consider as a linear classification model) and the only inputs you had were: X0 = 1 X1 = yesterday's share price X2 = the share price the day before that in X1 X3 = the share price the day before that in X2 X4 = the share price the day before that in X3 it would seem sensible to apply more of a weighting to the more recent share prices so you may decide to do a transform before applying the learning i.e., you may create a new matrix Z = [X0 X1*0.9 X2*0.8 X3*0.7 X4*0.6] and do the learning from Z. Hope this makes sense so far? My questions: 1) is this a sensible thing to do? 2) can the recency weights i.e., 0.9, 0.8, 0.7 and 0.6 be learned? More Advanced: Though this is a simple example, you may have more data each day for which you want to apply the same recency weighting i.e., you may have data for say (i) the minimum and (ii) the maximum price the share was on each day. In which case you may have a new model something like: X0 = 1 X1 = yesterday's share price X1_1 = the minimum price the share traded at yesterday X1_2 = the maximum price the share traded at yesterday X2 = the share price the day before that in X1 X2_1 = the minimum price the share traded the day before that in X1 X2_2 = the maximum price the share traded the day before that in X1 X3 = the share price the day before that in X2 X3_1 = the minimum price the share traded the day before that in X2 X3_2 = the maximum price the share traded the day before that in X2 X4 = the share price the day before that in X3 X4_1 = the minimum price the share traded the day before that in X3 X4_2 = the maximum price the share traded the day before that in X3 applying a new transform would be like this: Z = [X0 X1*0.9 X1_1*0.9 X1_2*0.9 X2*0.8 X2_1*0.8 X2_3*0.8 X3*0.7 X3_1*0.7 X3_2*0.7 X4*0.6 X4_1*0.6 X4_2*0.6] Hope this is still making sense? Extra questions: 3) is this still (if it was before) a sensible thing to do? 4) can the recency weights i.e., 0.9, 0.8, 0.7 and 0.6 be learned? Any pointers, discussion, answers much appreciated. 
#2




Re: Recency weighted regression
Unfortunately, if you are using a linear model, performing this recency weighting as you suggest will have no effect because you are going to rescale the inputvariables by weights and so this rescaling will get absorbed into the weights.
Suppose when you learn without rescaling you find weight ; now, when you rescale , your learned weight will just rescale in the inverse way ; your insample error will be the same, as will your outofsample error. You may have misunderstood the purpose of recency weighted regression; it is to differentially weight the error on different data points. In your case of stock prediction, it makes sense to weight the prediction error on the recenct days more than the prediction error on earlier days, hence the term recency weighted regression. Thus, if you let the input on day be ; the thing you are trying to predict on day is and the weights you learn are then the recency weighted error measure that one might wish to minimize is are the weights; to emphasize the recent data points more, you would chose to be increasing with . Quote:
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#3




Re: Recency weighted regression
Thanks for your response Dr Magdon it is really appreciated. I hope you don't mind me asking more questions (I suppose you won't answer if you don't want to lol).
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If I had a number of different company shares in my database and for each company I had 1000 days of their share price data, I would therefore be able to create approximately 996 training rows per company. Each training row containing the previous 4 days prices. To make simple, also assume I have managed to normalize each company's share prices so that they can be trained together (don't ask me how, this is just a made up example lol ) So because of this, I think I still need something along the lines of: [X0 X1 X1_1 X1_2 X2 X2_1 X2_3 X3 X3_1 X3_2 X4 X4_1 X4_2] per training row and a value of y which we will compare against. Going back to what you wrote, the recency weightings that I made up are useless here as they would be absorbed however, the learning algorithm would still pick up the important variables and thus give them the higher weights so in a way, I would hope the learning algorithm would implicitly work out anyway that the more "recent" variables would get larger weights i.e., W1... > W2... > W3... > W4 relatively speaking. Though I am sure when I test such a case it probably won't be as clean cut due to the problems associated with VC and degrees of freedom. Using the recency weights on the error as you suggested is a more failsafe way however I think I would then lose the structure I was hoping to use? Please can you confirm this in light of the additional model information I have presented? If so, maybe the following would work instead? I just do a linear regression on the entire (1000xNoOfCompanies) rows so that each day is treated independently, once I have found my optimum weights I use them to calculate for each row (I'm not sure about the squared bit?). These new values will then be grouped into a single row based on the "day" structure i.e., X0 = 1 X1 = X2 = X3 = X4 = a second bout of linear regression could then be used to work out the optimum "recency weights" for this new set (996xNoOfCompanies rows). This second idea, or yours (if still applicable wrt desired model structure?), would certainly help in terms of reducing degrees of freedom and so would definitely be preferable imo. 
#4




Re: Recency weighted regression
Quote:
With respect to your question though, you seem to be confusing two notions of recency: Let's take a simple example of one stock, which can generalize to the multiple stocks example. Suppose the stock's price time series is At time for you construct the input and the target . You would like to understand the relationship between and . If you know this relationship, you are can predict the future price from previous prices. So suppose you build a linear predictor . The learning task is to determine . To do this you minimize You will probably find that the weights in are not uniform. For example the weight multiplying might be the largest; this means that the most recent price is the most useful in predicting the next price . The notion of recency above should not be confused with recency weighted regression which is catering to the fact that the weights may be changing with time (that is in the stock example, the time series is nonstationary). To accomodate this fact you reweight the data points giving more weight to the more recent data points. Thus you minimize the error function The enforce that the more recent data points will have more contribution to and so you will choose a that better predicts on the more recent data points; in this way older data points play some role, but more recent data points play the dominant role in determining how to predict tomorrow's price. Thus in the example of time series prediction, there are these two notions of recency at play: (i) more recent prices are more useful for predicting tomorrows price (ii) the relationship between this more recent price and tomorrows price is changing with time (for example sometimes it is trend following, and sometimes reversion). In this case, more recent data should be used to determine the relationship between today's price and tomorrow's price.
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#5




Re: Recency weighted regression
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 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 () we would start with a dataset of the first 50 days... pseudocode: for i=50 to 996 step 1 ....... = wholeDataSet[items 1 to i] .......do the regression on and find by minimising .......error = error + endfor 
#6




Re: Recency weighted regression

#7




Re: Recency weighted regression
Yes, that would be a way to run the process and estimate how good the predictor is.
Quote:
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Have faith in probability 
#8




Re: Recency weighted regression
Thank you for all your help it has been really appreciated. I have one final question, do you know if there is a closed form solution to
(assuming is a vector with the same number of rows as x?) i.e., the closed form solution as used for linear regression and regularization  copied from lecture notes is this: I am not sure where would end up in the above, the derivation is beyond me mathematically? 
#9




Re: Recency weighted regression
Having spent some time on this, (this area of maths I am very weak)
I think the solution is: Where is a diagonal matrix. A bit like the Identity matrix but with weight values i.e., , 0, 0, ... 0 0, , 0 ... 0 .................. 0, 0, 0 ....  The bit that makes this tricky (for me) is the regularisation. I suppose I could test the above using this formula and then try the same using gradient descent (where I know it will be correct) if the values are close then the above can be considered correct (if I plug in largely varying values of lamba for testing). 
#10




Re: Recency weighted regression

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