Only if your model is a random walk or a simple mean model ,otherwise you will have to forecast out 16 periods for each of your 250 time series. You might want to take into account some factors like 1) day-of-the-week-effects 2 ) auto-projective structure i.e. the impact of previous values on the forecst 3) the impact of events like holidays such as lead,contempraneous and lag effects 4) possible changes in the model parameters over time 5) possible changes in the variance of the errors over time 6) level shifts in your time series 7) local time trends in your time series 8) the impact of unusual values ( one time anomalies ) . If you deal corrrectly with these eight considerations you forecast might be useful.

Only if your model is a random walk or a simple mean model ,otherwise you will have to forecast out 16 periods for each of your 250 time series. You might want to take into account some factors like 1) day-of-the-week-effects 2 ) auto-projective structure i.e. the impact of previous values on the forecast 3) the impact of events like holidays such as lead,contempraneous and lag effects 4) possible changes in the model parameters over time 5) possible changes in the variance of the errors over time 6) level shifts in your time series 7) local time trends in your time series 8) the impact of unusual values ( one time anomalies ) . If you deal corrrectly with these eight considerations you forecast might be useful.

Only if your model is a random walk of a simple mean model ,otherwise you will have to forecast out 16 periods for each of your 250 time series. You might want to take into account some factors like 1) day-of-the-week-effects 2 ) auto-projective structure i.e. the impact of previous values on the forecst 3) the impact of events like holidays such as lead,contempraneous and lag effects 4) possible changes in the model parameters over time 5) possible changes in the variance of the errors over time 6) level shifts in your time series 7) local time trends in your time series 8) the impact of unusual values ( one time anomalies ) . If you deal corrrectly with these eight considerations you forecast might be useful.

Only if your model is a random walk or a simple mean model ,otherwise you will have to forecast out 16 periods for each of your 250 time series. You might want to take into account some factors like 1) day-of-the-week-effects 2 ) auto-projective structure i.e. the impact of previous values on the forecst 3) the impact of events like holidays such as lead,contempraneous and lag effects 4) possible changes in the model parameters over time 5) possible changes in the variance of the errors over time 6) level shifts in your time series 7) local time trends in your time series 8) the impact of unusual values ( one time anomalies ) . If you deal corrrectly with these eight considerations you forecast might be useful.