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?

 A: Clean out the outlier instead of using a dummy variable (use tsclean()). 
Try AddTrig instead of AddSeasonal for there seasonal component, since your data seems to have multiple seasonalities. 
What other methods are you using that are giving better results than BSTS? 
A: Your approach is feasible but you need to accommodate many more columns (i.e. predictor series) than you have. I took your data into a comprehensive time series package that simultaneously deals with i.e. identifies 1) lead and lag effects around holidays 2) day-of-the-week effects and changes in day-of-the-week effects 3) time trends and level shifts 4) day-of-the-month effects % 5) week=of-the-month effects , 6) month-of-the-year effects 7) week-of-the-year effects 8) long-weekend effects 
9) anomalies 10) changes in error variance over time and others including user-specified/suggested causals et al and of course any necessary arima structure to deal with omitted structure.
This is the Actual/Fit and Forecast that you should be getting from a useful model  with model residuals here  and forecasts here for the next 365 days  .
Part of the equation is shown here  and here 
Hope this helps raise your expectations regarding daily modelling . solutions....
If you can find a way to identify these additional "columns" for your data you possibly might be able to something useful out of your current approach. Of course the trick is this do this automatically/programattically as I did.
Your "lack of confidence in your results" is echoed/mirrored by the "lack of confidence in your forecasts i.e. unrealistically very wide prediction limits "
In help to Alex , I have added more of the equation explicitely showing the indicator series for some of the Pulses ..
I was asked to provide a clear picture of the forecasts vis-a-vis the actuals


