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我能够使用Forecast()画面中的功能进行预测。我有每日每小时数据（需求电力）。以前我使用以下代码：

SCRIPT_REAL("library(forecast);
    jjearnts <- msts(.arg1, seasonal.periods=c(24, 7*24, 365*24));
    fit <- tbats(jjearnts);
    fcast <- forecast(fit,h=.arg2[1]);
    n<-length(.arg1);
    append(.arg1[(.arg2[1]+1):n],fcast$mean, after = n-.arg2[1])",
    SUM([ Load demand(electricity)]),[ Parameter])

上面的代码有效，但它缺少一些虚拟预测器。所以我尝试了以下代码：

SCRIPT_REAL("library(forecast);
    modelfitsample <- data.frame(.arg1,Weekday=rep(1:7,7);
    xreg <- cbind(Weekday=model.matrix(~as.factor(modelfitsample$Weekday));
    xreg <- xreg[,-1];
    colnames(xreg) <- c("Mon","Tue","Wed","Thu","Fri","Sat");
    jjearnts <-ts(modelfitsample$.arg1,frequency=24*365,start=c(2008,90));
    fcast <- forecast(jjearnts, h=.arg2[1]);
    n<-length(.arg1);
    append(.arg1[(.arg2[1]+1):n],fcast$mean, after = n-.arg2[1])",
    SUM([Load demand(electricity)]),[ Parameter])

此代码不起作用

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