Can statsforecast fit on a for a specific prediction window

Question

Can statsforecast fit on a for a specific prediction window <https nixtla github io statsforecast src core core html statsforecast fit> ```sf fit df = Y df ``` For instance currently the MSTL with AutoArima is not picking up any exogenous regressors If the prediction window is only the next step clearly the AR MA lags dominate but my objective is e g 48 hours ahead and there the covariates might prove beneficial How can I do this using statsforecast package Also is there some summary of fitted model similar to statsmodels Thanks so much

Kevin Kho · Answer

You can do ```sf fitted 0 0 model ```

Kevin Kho · Answer

I don t know about the 48 hours ahead Let me ask someone

Mariana Menchero · Answer

Hi < Matej> I was looking at your question but I m not sure I follow Do you want to fit the next 48 hours ahead in the historical data If that is the case then the cross validation method can help

Mariana Menchero · Answer

```crossvalidation df = sf cross validation df = df h = 48 step size = 48 n windows = 3 ``` With this you are fitting a MSTL model using all the information up to the cutoff date If you want to fit the whole historical data just change the number of windows to include all possible values If this is not what you need want please elaborate so we can help you with that

Matej · Answer

Apologies I got into time series only recently so safely correct me if I am talking nonsense I mean to ask if it is possible to fit the model with a specific forecast horizon in mind This might be beneficial as then the loss function likelihood can reflect the specific time horizon See for example the also legendary R package smooth <https openforecast org adam multistepLosses html> If I understand correctly the ETS model is then able to consider the forecast horizon h in its estimation of parameters Specifically the General Predictive Likelihood GPL loss or others Thanks for being so helpful

Matej · Answer

Also Is it somehow possible to parallelize the sf cross validation When I run it the process is only able to utilize one CPU but that might be an issue on my side running it in jupyter or something like that

Matej · Answer

```sf fitted 0 0 model ``` for MSTL this only returns the components ```data trend seasonal remainder ``` I assume I have to calculate the summary such as likelihood and R^2 by myself out of these numbers or out of ```insample forecasts = sf forecast fitted values ``` Sorry for asking dumb questions some things are not obvious from the documentation

Brian Head · Answer

< Matej> Don t see an answer to your question about parallelizing CV I was able to do it in Jupyter and also Databricks by using n jobs in the StatsForecast call that pipes into cross validation I m not an expert with statsforecast just getting into it from the R world but parallelization and also distributed processing were important for me too