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[英]Timeseries Crossvalidation in R: using tsCV() with tslm()-Models
[英]Forecasting timeseries with tslm in R
我還是R的新手,我正面臨一個似乎無法解決的問題。
我想預測我的時間序列數據。 我有今年的每日數字:y,以及我想用作預測的去年的每日數字。 數字顯示周周期。 我試過這段代碼。 (清晰的假號)
x = rnorm(60,0,1)
y = rnorm(60,0 ,1) + 2*cos(2*pi*1:60/7) + 10*x
new_x = rnorm(10,0,1)
y <- ts(y,frequency = 7)
fit <- tslm(y ~ trend + season + x)
fcast = forecast.lm(fit, h = 10, newdata = new_x)
我收到錯誤消息:
Error in model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels) :
variable lengths differ (found for 'x')
In addition: Warning message:
'newdata' had 10 rows but variables found have 60 rows
我做錯了什么提示?
從你的fit
對象:
Call:
lm(formula = formula, data = "y", na.action = na.exclude)
Coefficients:
(Intercept) trend season2 season3 season4 season5 season6 season7 x
1.1644029 0.0009672 -1.5575562 -3.6723105 -3.1824001 -1.5658857 0.0789683 0.3053541 9.9233635
最后一個變量名為x
。 而對於forecast.lm
的幫助說newdata
是一個可選的data.frame。 您需要將new_x
轉換為data.frame,其中x
為列名。
library(forecast)
x = rnorm(60,0,1)
y = rnorm(60,0 ,1) + 2*cos(2*pi*1:60/7) + 10*x
new_x = rnorm(10,0,1)
y <- ts(y,frequency = 7)
fit <- tslm(y ~ trend + season + x)
# You can directly use `forecast`, as `fit` is an lm object
# and you don't need `h`, as you provide new data.
fcast = forecast(fit, newdata = data.frame(x=new_x))
# Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
# 9.571429 -3.1541222 -4.5886075 -1.719637 -5.37216743 -0.9360771
# 9.714286 12.5962250 11.1367496 14.055700 10.33953926 14.8529108
# 9.857143 10.5924632 9.1480030 12.036924 8.35899443 12.8259321
#10.000000 15.9419378 14.4775444 17.406331 13.67764776 18.2062278
#10.142857 -7.1887433 -8.6444741 -5.733013 -9.43963897 -4.9378477
#10.285714 -9.4133170 -10.8470152 -7.979619 -11.63014523 -7.1964887
#10.428571 2.2702132 0.8331488 3.707278 0.04818005 4.4922464
#10.571429 0.3519401 -1.1037991 1.807679 -1.89896851 2.6028487
#10.714286 -11.8348209 -13.2930857 -10.376556 -14.08963475 -9.5800070
#10.857143 1.0058209 -0.4435763 2.455218 -1.23528154 3.2469233
您可以將new_x轉換為data.frame,並且您的初始代碼也可以正常工作。
new_x變量的類型為number,需要將data.frame作為forecast.lm的輸入。
問候,
Ganesh Bhat
錯誤似乎很明顯:
new_data有10個隨機變量,而y&x有60個。你能更新new_data有60個隨機變量並驗證錯誤是否沒有發生?
問候,
Ganesh神
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