Solved – How to improve forecast accuray of bsts model

I have a question about the use of the bsts package. In general my question is if my approach is feasible. Because my holdout MAPE is much worse than all the other approaches I have in my ensemble.

Here is my code.

library("bsts") library("ggplot2") library("reshape") # split into test and train ------------------------------------------------------ date <- as.Date("2017-06-04") horizon <- 105 model.data$DATUM <- as.Date(model.data$DATUM) xtrain <- model.data[model.data$DATUM <= date,] xtest <- model.data[model.data$DATUM > date,]  # building the first model ------------------------------------------------------ ss <- list() ss <- AddSemilocalLinearTrend(ss, xtrain$ITEMS) ss <- AddSeasonal(ss,xtrain$ITEMS,nseasons = 52,                   season.duration = 7)  # V7 is a dummy variable for the one outlier fit <- bsts(ITEMS ~ V7 ,             data = xtrain,             seed = 100,             state.specification = ss,             niter = 1500)  # validation -------------------------------------------------------------------- burn <- SuggestBurn(0.1,fit) fcast.holdout <- predict(fit,                          newdata = xtest,                          h = horizon,                          burn = burn)  validation.time <- data.frame("semi.local.linear.bsts" = as.numeric(fcast.holdout$mean),                               "actual" = model.data[model.data$DATUM > date,"ITEMS"],                               "datum" = model.data[model.data$DATUM > date,"DATUM"])  a <- melt(validation.time,id.vars = c("datum")) ggplot(data = a,        aes(x = datum, y = value, group = variable,color = variable))+        geom_point()+        geom_line()  plot(fcast.holdout) 

The data can be found here. The data are daily sales data for a retail shop. Later I want to include some dummy variables which you can also find in the example data.

For me the main questions are:

Is the seasonal part correctly defined? I have a annual seasonality in my data and also a weekly pattern. However in the validation plot I cannot find the weekly pattern.

Why do I have such high prediction intervals? Should I change the trend part?

Similar Posts:

• Solved – How to improve forecast accuray of bsts model
• Solved – Time series with multiple variables and different start date
• Solved – 10-fold cross validation or hold out method
• Solved – How to plot a stair steps function with ggplot
• Solved – the difference between k-holdout and k-fold cross validation