The thesis begins with a review of different methods of time series modelling in Chapters 2-4. Chapter 2 presents a mathematical point of view to the subject. These ideas form the foundation for what follows but most of the results are not used directly. Statistical methods for learning the parameters of the different models are presented in Chapter 3. Chapter 4 introduces the building blocks of the switching NSSM and discusses previous work on HMMs and SSMs, as well as some of their combinations.
The second half of the thesis (Chapters 5-7) consists of the development of the switching NSSM and experimental verification of its operation. In Chapter 5, the exact structures of the parts of the switching model, the HMM and the NSSM, are defined. It is also described how the two models are combined. The ensemble learning based learning algorithms for all the models of the previous chapter are derived in Chapter 6. A series of experiments was conducted to verify the performance of the model. Chapter 7 discusses these experiments and their results. Finally the results of the thesis are summarised in Chapter 8.
The thesis also has two appendices. Appendix A presents two important probability distributions, the Gaussian and the Dirichlet distribution, and some of their most important properties. Appendix B contains a derivation of a result needed in the learning algorithm in Chapter 6.