Neural, Novel & Hybrid Algorithms for Time Series Prediction
Published on 20. October 1995
Book
Mixed media product
XX, 514 pages
978-0-471-13041-3 (ISBN)
Description
An authoritative guide to predicting the future using neural, novel, and hybrid algorithms Expert Timothy Masters provides you with carefully paced, step-by-step advice and guidance plus the proven tools and techniques you need to develop successful applications for business forecasting, stock market prediction, engineering process control, economic cycle tracking, marketing analysis, and more.
Neural, Novel & Hybrid Algorithms for Time Series Prediction provides information on: Robust confidence intervals for predictions made with neural, ARIMA, and other models Wavelets for detecting features that presage important events Multivariate ARMA models for simultaneous prediction of multiple series based on multiple inputs and shocks Hybrid ARMA/neural models to improve the accuracy of predictions Data reduction and orthogonalization using principal components and related operations Digital filters for preprocessing to enhance useful information and suppress noise Diagnostic tools such as the maximum entropy spectrum and Savitzky-Golay filters for suggesting and validating prediction models Effective preprocessing techniques for prediction with neural networks CD-ROM INCLUDES: PREDICT-both DOS and Windows NT versions-a powerful time series program that can be easily customized to make accurate predictions in any application area Much useful source code, including the complex-general multivariate fast Fourier transform in both C++ and Pentium-optimized assembler
Neural, Novel & Hybrid Algorithms for Time Series Prediction provides information on: Robust confidence intervals for predictions made with neural, ARIMA, and other models Wavelets for detecting features that presage important events Multivariate ARMA models for simultaneous prediction of multiple series based on multiple inputs and shocks Hybrid ARMA/neural models to improve the accuracy of predictions Data reduction and orthogonalization using principal components and related operations Digital filters for preprocessing to enhance useful information and suppress noise Diagnostic tools such as the maximum entropy spectrum and Savitzky-Golay filters for suggesting and validating prediction models Effective preprocessing techniques for prediction with neural networks CD-ROM INCLUDES: PREDICT-both DOS and Windows NT versions-a powerful time series program that can be easily customized to make accurate predictions in any application area Much useful source code, including the complex-general multivariate fast Fourier transform in both C++ and Pentium-optimized assembler
More details
Person
TIMOTHY MASTERS received his PhD in mathematics in 1981. Since then he has worked as a consultant to the defense community and to industry. He is the author of Practical Neural Network Recipes in C++; Signal and Image Processing with Neural Networks: A C++ Sourcebook and Advanced Algorithms for Neural Networks: A C++ Sourcebook. His current research focuses on high-level image understanding using artificial intelligence and neural networks.
Content
Preprocessing. Subduing Seasonal Components. Frequency-Domain Techniques I: Introduction. Frequency-Domain Techniques II: Filters and Features. Wavelet and QMF Features. Box-Jenkins ARMA Models. Differencing. Robust Confidence Intervals. Numerical and Statistical Tools. Neural Network Tools. Using the NPREDICT Program. Appendices. Bibliography. Index.