Hi mailinglist members,
Im actually working on a time series prediction and my current approach is to decompose the series first into a trend, a seasonal component and a remainder. Therefore Im using the stl() function. But Im wondering how to get the single components in order to predict the particular fitted series. This code snippet illustrates my problem: series <- vector(length=300) noise <- rnorm(300,0,2) time <- 1:300 series[1] <- noise[1] for(i in 3:300){ series[i] <- 0.5*series[i-1]+ noise[i] + 0.01*time[i] } seriesTs <- ts(series, start=c(1980,1), frequency=12) decomp <- stl(seriesTs ,"periodic") plot(decomp) llrSaison <- loess(seriesTs~time , span=decomp$win[1] , degree=decomp$deg[1]) llrTrend <- loess(seriesTs~time, span=decomp$win[2] , degree=decomp$deg[2]) plot(llrSaison$fitted) The last plot differs much from the seasonal plot in the plot(decomp) call. This is why the llr estimator doesnt extract the seasonal component, but how can I predict the single components at last? Or is there a function which can predict the values of the stl-object. Predict() doesnt work, Ive already tried it. All the best, Konrad Hoppe [[alternative HTML version deleted]]
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