This book is based on a PhD study/thesis that proposed novel adaptive methods to discover and model dynamic pattern of interactions in multiple time-series to not only predict their future values but also to extract knowledge about their joint movement. Three distinct methods of multiple time-series analysis are developed based on different concepts of learning (inductive and transductive reasoning) which are capable of modelling the dynamics of interaction between variables in a specific setting. As each approach addresses the problem of multiple time-series analysis and modelling from a different perspective and since each has its own predictive power, an integrated multi-model framework that incorporates the different approaches through a dynamic contribution adjustment function is also proposed. Results from two case studies suggest that (a) the proposed methods are capable of modelling dynamic pattern of interactions between variables and (b) the idea of including the nature and strength of relationship between a collection of time sensitive variables into a prediction model appears to be beneficial to the problem of multiple time-series prediction.
