Combining domain knowledge and statistical models in time series analysis

Abstract

This paper describes a new approach to time series modeling that combines subject-matter knowledge of the system dynamics with statistical techniques in time series analysis and regression. Applications to American option pricing and the Canadian lynx data are given to illustrate this approach.

Hidden Behavior Prediction of Complex Systems Based on Hybrid Information

“…If some qualitative knowledge about a system is known, the qualitative knowledge-based method, such as expert system-based method [1] and Petri net-based method [30], can be used to infer the hidden behavior of the system. If the hybrid information is available, a number of theoretical methods have been employed to use the hybrid information for predicting the hidden behavior, such as hidden Markov model (HMM) [2], [8], [12], [15], [16], [32]- [34], [37], dynamic Bayesian network (DBN) [9], [13], [17], [19], [22], domain knowledge and statistical model-based method [10], qualitative knowledge and linear partial least square-based method [11], and fuzzy logic and artificial neural network (FL-ANN)-based method [18], [25]. The main steps of the hybrid information-based method are: the qualitative knowledge is first used to establish the initial forecasting model, and then the quantitative data are employed to train the forecasting model so that the future behaviors can be predicted accurately.…”