Comparison of prediction using matching pattern and state space models

Abstract EN

Predicting future behaviour is one of the important topics in statistical sciences due to the need for it in different areas of life, and most countries rely on their development programs on advanced scientific foundations and methods in order to reach more effective results.

this research deals with a comparison of the accuracy of time series prediction using state space models and the matching patterns method of Singh (2001) algorithm by applying to real data, which are economic observations that were previously addressed by the researchers box and Jenkins (1976).

where the inputs represent the leading indicator and the outputs represent sales, and the importance of this research is represented in knowing the most accurate method for predicting time series.

the matlab program has been used to access the results of the research.

the most important results of the research are that the state space model is more accurate in forecasting than the matching patterns in the studied data because it has the lowest values of the test criteria of prediction accuracy results.