Comparison of prediction using Matching Pattern and state space models.
Abstract
Predicting future behavior 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.
References
- AlKayat, Bassel Thanoon, (2011), Markovian modeling with practical applications Dar Iben Alhaytham For printing and publishing,Unversity of Mosul.
- Tohme ,S.K., (2012), Using Analysis of Time Series to Forecast numbers of The Patients with Malignant Tumors in Anbar Provinc, Anbar University Journal of Economic and Administrative Sciences, Vol.4, No.8.
- Box,G.E.P., & Jenkines, G.M. (1976), Time Series Analysis Forecasting and Control, Holden Day Inc. 500 Sansome Street San Francisco California U.S.A.
- Box, G., Jenkins, G., Reinsel ,G. and Ljung G., (2016)," Time Series Analysis Forecasting and control", John wiley & Sons , Inc . Hoboken, New Jersey.
- Guerbyenne, H., & Hamdi, F., (2014), Bootstrapping Periodic State-Space, Communications in Statistics-Simulation and Computation.
- Hayawi, H.A.A. (2022), Using wavelet in identification state space models, Int. J. Nonlinear Anal. (1), 2573-2578.
- Kalman, R.E., (1960), A New Approach to Linear Filtering and Prediction Problem Trans ASME series, Journal of Basic Engineering (82) 35-45.
- Nelles, O., (2001), Nonlinear System Identification from Classical Approach to Neural Network and Fuzzy Models, Springer Verlag Belin Heidelberg Germany.
- Reference
- Singh,S., (2001), Multiple Forecasting using Local Approximation, Journal Pattern Recognition,. (34) 443-455.
- Singh ,S., (2000), Pattern Modelling in Time-series Forecasting, Cybernetics And Systems An International Journal ,. (31) 1-25.
- Ibrahim, N.J., & Hayawi, H.A.A. (2021), Employment the State Space and Kalman Filter Using ARMA models, IJASEIT, 11(1), 145-149.
