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A forecasting procedure for nonlinear autoregressive time series models
Author(s) -
Cai Yuzhi
Publication year - 2005
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.959
Subject(s) - autoregressive model , series (stratigraphy) , nonlinear system , time series , computer science , variance (accounting) , monte carlo method , range (aeronautics) , star model , autoregressive integrated moving average , econometrics , mathematics , algorithm , statistics , machine learning , economics , engineering , paleontology , physics , accounting , quantum mechanics , biology , aerospace engineering
Forecasting for nonlinear time series is an important topic in time series analysis. Existing numerical algorithms for multi‐step‐ahead forecasting ignore accuracy checking, alternative Monte Carlo methods are also computationally very demanding and their accuracy is difficult to control too. In this paper a numerical forecasting procedure for nonlinear autoregressive time series models is proposed. The forecasting procedure can be used to obtain approximate m ‐step‐ahead predictive probability density functions, predictive distribution functions, predictive mean and variance, etc. for a range of nonlinear autoregressive time series models. Examples in the paper show that the forecasting procedure works very well both in terms of the accuracy of the results and in the ability to deal with different nonlinear autoregressive time series models. Copyright © 2005 John Wiley & Sons, Ltd.