On The Joys Of Re-reading Our Old Books

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Since I first wrote on this subject in my Facebook, (see image of text of the FB post above), I have discovered yet another classic: Neural, Novel and Hybrid Algorithms for Time Series prediction~ by Timothy Masters [Wiley, 1995]. It's eighteen years since the book was first published. But the topics covered (especially on data pre-processing, scaling and transformations) are still as important. I read each page with the new advantage of perspectives and practical experience gained on this subject these past eighteen years. Old words took on a new significance, a deeper meaning, and sometimes a different meaning. I begin to see the advantages of of hybrid models that combine Neural Nets, Genetic Algorithms, Statistics and Digital Signal Processing. Here are the Contents of this book:
1. Preprocessing
2. Subduing Seasonal Components
3. Frequency-Domain Techniques I
4. Frequency-Domain Techniques II
5. Wavelet and QMF Features
6. Box-Jenkin ARMA Models
6. Differencing
7. Robust Confidence Intervals
8. Numerical and Statistical Tools 
9. Neural Network Tools
10. Using the NPREDICT Program ( Program diskette enclosed with book)

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