R 预测::thetaf错误
我目前在forecast package 8.2和thetaf功能方面存在问题:R 预测::thetaf错误,r,time-series,forecasting,R,Time Series,Forecasting,我目前在forecast package 8.2和thetaf功能方面存在问题: dat<- structure(c(5, 0, 5, 0, 0, 2, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 2, 0, 1, 0, 2.1, 0, 2, 0, 1, 0, 0, 0, 2.5, 2, 2, 0, 1.7, 0, 1.5, 0, 1, 0, 0, 0, 2
dat<- structure(c(5, 0, 5, 0, 0, 2, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 2, 0, 1,
0, 2.1, 0, 2, 0, 1, 0, 0, 0, 2.5, 2, 2, 0, 1.7, 0, 1.5, 0, 1,
0, 0, 0, 2.5, 0), .Tsp = c(1999, 2003.91666666667, 12), class = "ts")
library(forecast)
thetaf(dat,h = 1)$mean
给出以下错误
Error in ets(object, lambda = lambda, allow.multiplicative.trend = allow.multiplicative.trend, :
y should be a univariate time series
Additional: Warnings:
1: In ets(x, "ANN", alpha = alpha, opt.crit = "mse", lambda = lambda, :
Missing values encountered. Using longest contiguous portion of time series
2: In fit$call <- match.call() : ...
误差与频率有关。如果它是1而不是12,那么它就工作了。我看不出这会导致错误的原因。有什么想法吗 当应用于季节性数据时,θ方法使用乘法经典分解来去除季节性。在这种情况下,分解失败:
> decompose(dat, type="multiplicative")
$x
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999 5.0 0.0 5.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0
2000 4.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2001 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9 0.0 2.0 0.0
2002 1.0 0.0 2.1 0.0 2.0 0.0 1.0 0.0 0.0 0.0 2.5 2.0
2003 2.0 0.0 1.7 0.0 1.5 0.0 1.0 0.0 0.0 0.0 2.5 0.0
$seasonal
Jan Feb Mar Apr May Jun Jul Aug
1999 5.5063443 0.0000000 1.2804721 0.0000000 1.2020131 0.0000000 0.2851915 0.0000000
2000 5.5063443 0.0000000 1.2804721 0.0000000 1.2020131 0.0000000 0.2851915 0.0000000
2001 5.5063443 0.0000000 1.2804721 0.0000000 1.2020131 0.0000000 0.2851915 0.0000000
2002 5.5063443 0.0000000 1.2804721 0.0000000 1.2020131 0.0000000 0.2851915 0.0000000
2003 5.5063443 0.0000000 1.2804721 0.0000000 1.2020131 0.0000000 0.2851915 0.0000000
Sep Oct Nov Dec
1999 0.7674245 0.0000000 2.1696136 0.7889410
2000 0.7674245 0.0000000 2.1696136 0.7889410
2001 0.7674245 0.0000000 2.1696136 0.7889410
2002 0.7674245 0.0000000 2.1696136 0.7889410
2003 0.7674245 0.0000000 2.1696136 0.7889410
$trend
Jan Feb Mar Apr May Jun Jul Aug
1999 NA NA NA NA NA NA 0.9583333 0.9166667
2000 0.3333333 0.3333333 0.3333333 0.3333333 0.3333333 0.3333333 0.1666667 0.0000000
2001 0.0000000 0.0000000 0.0375000 0.0750000 0.1583333 0.2416667 0.2833333 0.3250000
2002 0.7083333 0.7500000 0.7125000 0.6750000 0.6958333 0.8000000 0.9250000 0.9666667
2003 0.8916667 0.8916667 0.8916667 0.8916667 0.8916667 0.8083333 NA NA
Sep Oct Nov Dec
1999 0.7083333 0.5000000 0.5000000 0.4166667
2000 0.0000000 0.0000000 0.0000000 0.0000000
2001 0.4125000 0.5000000 0.5833333 0.6666667
2002 0.9500000 0.9333333 0.9125000 0.8916667
2003 NA NA NA NA
$random
Jan Feb Mar Apr May Jun Jul Aug
1999 NA NaN NA NaN NA NA 0.0000000 NaN
2000 2.1793043 NaN 0.0000000 NaN 0.0000000 NaN 0.0000000 NaN
2001 NaN NaN 0.0000000 NaN 0.0000000 NaN 0.0000000 NaN
2002 0.2563887 NaN 2.3017827 NaN 2.3911982 NaN 3.7907196 NaN
2003 0.4073466 NaN 1.4889369 NaN 1.3995214 NaN NA NaN
Sep Oct Nov Dec
1999 0.0000000 NaN 0.0000000 0.0000000
2000 NaN NaN NaN NaN
2001 2.8430397 NaN 1.5802682 0.0000000
2002 0.0000000 NaN 1.2627714 2.8430397
2003 NA NaN NA NA
$figure
[1] 5.5063443 0.0000000 1.2804721 0.0000000 1.2020131 0.0000000 0.2851915 0.0000000
[9] 0.7674245 0.0000000 2.1696136 0.7889410
$type
[1] "multiplicative"
attr(,"class")
[1] "decomposed.ts"
问题的产生是因为4月、6月、8月和10月的季节性指数为零,因此除以季节性指数会产生NaN值
我已经修复了forecast软件包v8.3中的问题,以便在出现这种情况时继续使用非季节性版本的θ方法。如果零是问题所在,那么如果我将频率降低到1,该函数为什么会起作用?