NuclearPhynance Archive

Duda -Hart

& Fukinaga are the classic references.



But as Jslade mentions the problem with financial data is it is very noisy so a lot of the techniques are an overkill. From talking to people who are successfully doing this it appears to me that linear models are all that you need , you need to spend time preprocessing/cleaning your data but all this fancy machinery can kill you. (Caveat emptor we don't do anything using ML right now so a practitioner here will quickly correct me.)

bluelou- Thank you very much for taking time in offering the suggestion.

bluelou- Thank you very much for taking time in offering the suggestion.

gax- Thank you very much for your suggestion.

jslade- Thank you very much for your views and thank you very much for taking time in replying the post.

FWIIW, I bought the Kay books HankScorpio suggested: these are wonderful. If every book were like these, I'd be out of a job. They're actual cookbooks. Thanks HS!

The book suggested by gax (Pattern Theory by Mumford) is as described; beautiful. I don't think it is very practical, but one could learn a lot from it. It's kind of a philosophy book.

These are great books, agreed, but they are not signal processing books in the classical sense and do not really represent what most people understand by the notion of signal processing. Feel free to contradict, as I am by no means an expert... The scope of these is remarkably similar to many econometrics texts.

jslade, you're welcome.



mtsm, would you care to elaborate further on what you believe people understand when they refer to SP in the "classical sense"? Perhaps you could provide some books for examples.



Maybe it's because I'm an EE by training, but what is described in Kay's books would be what I'd be thinking of when referring to SP.

I concur with jslade about the books by Steven Kay.



The chapters are quiet straightforward, the language is very easy to understand and most importantly the problems at the end of chapter are quiet testing which is what I was looking for.



Also, a large number of figures have been used to explain important concepts.



To sum up, the books are indeed excellent.



Thank you once again, HankScorpio, for recommending the books.



A

Well I was more thinking along the lines of empirical signal analysis, filtering, spectral analysis, time-frequency analysis, wavelets, you know the usual stuff. There are a lot of DSP books out there. As a failed physicist I am thinking for example of the Bendat, Piersol book, 'Analysis of Random Data', that is not to cite any digital signal processing references that I don't really know about.