I am using the bfastlite() function of the bfast package to run a time-series (ts) analysis. From the author’s paper (table 2), I quote:

Needs parameter tuning to optimise performance, does not differentiate
between breaks in seasonality and trend

So far, I was fine-tuning the model manually, that is, I was changing the parameters one by one, which is time-consuming. Does someone have a better solution regarding the fine-tuning of the model?

To see which parameters of the model achieve the best results, I was checking the dates in the detected breakpoints (visual inspection).

To see which parameters of the model achieve the best results, I was checking the dates in the detected breakpoints (visual inspection). I am not sure if that method (visual inspection) is appropriate.

If this question sound a bit vague, so let me expand a little bit. After running the bfastlite() using the default parameters (i.e., bp = bfastlite(datats)), we get a result. Is there a way to measure (something like rmse, or r-squared) how well the algorithm modeled the ts? What I basically mean is that if there is an index equivalent to let’s say rmse when someone is running a linear regression. For example, what if the parameter breaks with BIC instead of LWZ detects more accurate the breakpoints (by visually inspecting the detected breakpoints)? Apart from the visual inspection, shouldn’t be some other way to measure the performance of the model?

Based on the above, is there a more efficient way to optimize the parameters of the model (something like a function tune_bfastlite())? What do I mean by optimizing the parameters? I think with an example I can explain it better. When someone is tuning a random forest model, he/she can perform a full grid search to find the optimal parameters of the model (mtry, number of trees, etc) by searching all the possible combinations and for each combination he/she checks the rmse (or mse, r-squared). Is this what the authors of the paper meant when they said “Needs parameter tuning to optimise performance“?

library(bfast)

plot(simts) # stl object containing simulated NDVI time series
datats <- ts(rowSums(simts$time.series))
# sum of all the components (season,abrupt,remainder)
tsp(datats) <- tsp(simts$time.series) # assign correct time series attributes
plot(datats)

# Detect breaks. default parameters
bp = bfastlite(datats)
plot(bp)

# optimized model ??????
bp_opt <- bfastlite()

R 4.4.1, bfast 1.6.1, Windows 11.