8.3 Further Reading
System identification and time series prediction are broad and diverse fields. The following list is a small sampling of the vast literature available on these topics.
The following books are good introductions:
L. Ljung and T. Glad (1994), Modeling of Dynamic Systems, Prentice Hall, Englewood Cliffs, NJ.
R. Johansson (1993), System Modeling and Identification, Prentice Hall, Englewood Cliffs, NJ.
The following books are more thorough and they are used in graduate courses at many universities:
L. Ljung, (1999), System Identification: Theory for the User, Second Edition, Prentice Hall, Englewood Cliffs, NJ.
T. Söderström and P. Stoica (1989), System Identification, Prentice Hall, Englewood Cliffs, NJ.
The following article discusses possibilities and problems using nonlinear identification methods from a user's perspective:
J. Sjöberg et al. (1995), Non-Linear Black-Box Modeling in System Identification: A Unified Overview, Automatica, vol. 31. no. 12, pp. 1691-1724.
This book is a standard reference:
G.E.P. Box and G.M. Jenkins (1976), Time Series Analysis, Forecasting and Control, Holden-Day, Oakland, CA.
Many modern approaches to time series prediction can be found in this book and in the references therein:
A.S. Weigend and N.A. Gershenfeld (Editors) (1994), Time Series Prediction: Forecasting the Future and Understanding the Past, Addison-Wesley, Proceedings of the NATO Advanced Research Workshop on Comparative Time Series Analysis Held in Santa Fe, New Mexico, May 14-17, 1992.
Standard books on neural networks might also be of some interest, for example, the following:
J. Herz, A. Krough, R.G. Palmer (1991), Introduction to the Theory of Neural Computation, Addison-Wesley, Reading, MA.
S. Haykin (1999), Neural Networks: A Comprehensive Foundation, Second Edition, Macmillan, New York.