Sunday, 6 October 2013

Update on Savitzky-Golay Filters

For the past couple of weeks I have been playing around with Savitzky-Golay filters with a hope of creating a moving endpoint regression line as a form of zero lag smoothing, but unfortunately I have been unable to come up with anything that is remotely satisfying and I think I'm going to abandon this for now. My view at the moment is that SG filters' utility, for my purposes at least, is limited to feature extraction via the polynomial coefficients to get the 2nd, 3rd and 4th derivatives as inputs for my Neural net classifier.

To do this will require a time consuming retraining period, so before I embark on this I'm going to use what I think will be an immediately useful SG filter application. In a previous post readers can see three screenshots of moving windows of my idealised price series with SG filters overlaid. The SG filters follow these windowed "prices" so closely that I think SG filters more or less == windowed idealised prices and features derived there from. However, when it comes to applying my currently trained NN the features are derived from the noisy real prices seen in the video of the above linked post. What I intend to do wherever possible is to use a cubic SG filter applied to windowed prices and then derive input features from the SG filter values. Effectively what I will be doing is coercing real prices into smooth curves that far more closely resemble the idealised prices my NN was trained on. The advantage of this is that I will not have to retrain the NN, but only refactor my feature extraction code for use on real prices to this end. The hope is, of course, that this tweak will result in vastly improved performance to the extent that I will not need to consider future classifier NN retraining with polynomial derivatives and can proceed directly to training other NNs.

Finally, on a related note, I have recently been directed to this paper, which has some approaches to time series classification that I might incorporate in future NN training. Fig. 5 on page 9 shows six examples of time series classification categories. My idealised prices categories currently encompass what is described in the paper as "Cyclic," "Increasing Trend" and "Decreasing Trend." I shall have to consider whether it might be useful to extend my categories to include "Normal," "Upward Shift" and "Downward Shift." 


Anonymous said...

Is there any evidence that these pattern classifications have predictive power?

Dekalog said...


I've not actually tested predictive power of SG filters as yet because I've been concentrating simply on classification. Classification per se doesn't necessarily imply a directional bias strong enough to take a trade. All my work on classification so far has been with a view to creating a framework for decision making, via neural nets. However, your question is interesting enough to at least merit a simple investigation, which I shall do in the very near future and then post the results.