time seris prediction using AR(1) model
1 次查看(过去 30 天)
显示 更早的评论
Hi all, I want to forecast/ predict a time series based on first order Auto Regressive modeles,AR(1). The time series which called "q". For this aim I have done steps below:
- Time series standardization using mean and standard deviation.
- estimation of AR(1) parameter.
The computer programming for above operations is below:
clc; clear;
close all;
q=[50 38 31 24 37 56 54 39 40 38 59 89 41 42 42 26 38 26 25 20 28 33 23]';
mue=mean(q);
stdvn=std(q);
q_standardized =(q-mue)./stdvn;
ar_parameter= ar(q_standardized,1)
present(ar_parameter);
The output of the program is:
Discrete-time IDPOLY model: A(q)y(t) = e(t(
A(q) = 1 - 0.4559 q^-1
Loss function 0.773834 and FPE 0.841124
Sampling interval: 1
Discrete-time IDPOLY model: A(q)y(t) = e(t)
A(q) = 1 - 0.4559 (+-0.1924) q^-1
I need to predict 1000 numbers for t+1 time step using "q" set, which called "qt+1", and follows AR(1) models parameter. How can I do it. |Any help would be appreciated.
0 个评论
回答(0 个)
另请参阅
类别
在 Help Center 和 File Exchange 中查找有关 Linear Model Identification 的更多信息
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!