Open AccessAn Improved Prediction Model Combining Inverse Exponential Smoothing and Markov Chain
Author(s) -
Tong Niu,
Lin Zhang,
Bo Zhang,
Bofan Yang,
Shengjun Wei,
Paolo Manfredi
Publication year - 2020
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2020/6210616
Subject(s) - exponential smoothing , markov chain , smoothing , exponential function , inverse , mathematics , computer science , mathematical optimization , value (mathematics) , markov model , algorithm , statistics , mathematical analysis , geometry
On the basis of the triple exponential smoothing prediction model, this paper introduces the reverse prediction idea and establishes the reverse triple exponential smoothing model by setting parameters such as threshold value and iteration times and reasonably correcting its initial value. This method can effectively reduce the error of early prediction value. At the same time, aiming at the problem that the predicting advantage of the reverse triple exponential smoothing model weakens in the later period, Markov theory is introduced to correct its error value, and an improved prediction model combining inverse exponential smoothing and Markov chain is further established. The improved model combines the advantages of index model trend prediction and Markov fluctuation prediction, and the prediction accuracy and stability of the model are significantly improved through case tests.