使用arima来预测时间序列

Question

我尝试使用ARIMA来预测时间序列。

ts <- c(283.678,278.158,273.345,269.773,265.863,265.673,262.977,272.557,267.628,270.106,276.346,292.736,310.649,320.550,332.954,350.313,361.524,367.406,369.442,372.043,365.030,375.210,371.006,364.991,359.975,365.849,373.687,368.167,368.281,366.982,365.752,366.844,356.526,356.667,354.937,352.461,353.742,357.139,362.981,384.613,405.519,400.974,388.696,374.536,348.781,324.664,307.051,297.943) 
ar <- arima(ts) 
ps <- predict(ar, 48) 
plot(c(ts, ps$pred), type="l") 

为什么这段代码能够预测平滑线而不是一些相似的曲线？

Answer 1

因为忘记添加变量order在arima()，因此装配的ARIMA(0,0,0)模型，这仅仅是一个常数截距项。

如果你想自动适应的适当的顺序（由AIC，AICC和BIC选择），您可以使用auto.arima()从forecast包的ARIMA模型：从参数

library(forecast) 
ar <- auto.arima(ts) 
ps <- predict(ar, 10) 
ps 
# $pred 
# Time Series: 
# Start = 49 
# End = 58 
# Frequency = 1 
# [1] 291.7978 287.6517 284.8543 282.9668 281.6934 280.8342 280.2545 279.8634 279.5995 279.4214