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Given that your coefficients are statistically significant and your model's errors have passed multiple "model specification tests" then I would obtain confidence limits by re-samplimg the model's err …

What you are proposing is to shoe-horn say L particular models while optimally estimating parameters for a given set of historical observations (N) and compute an out-of-sample mape/smape given a spec …

The confidence limits for an ARIMA forecast are based upon the PSI WEIGHTS . The PSI WEIGHTS are easily computed by representing the ARIMA MODEL as a pure MOVING AVERAGE MODEL. One should not be depen …

We implemented a daily forecasting model that included day-of-the-week;week-of-the-year;month-of-the year effects AND the lead/contemporaneous and lag effects around known events AND any Level Shifts/Time … What you want to do is to also include an hour-of-the-day forecasting model which would in conjunction with the daily forecast/model produce an estimate of the current day's total and the total for the …

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 …

You might want to look at http://www.autobox.com/cms/index.php/news/47-can-forecasting-help-me-staff-a-specific-call-center- for some guidance. …

The problem I believe is that you want to forecast out the expected value for 48 half-hour intervals for some foreseeable period of time. I am currently working with a major fast-food franchise to pre …

An appropriate model for this data is a simple AR(1). The whole idea is to NOT assume a model NOR to ignore unusual values BUT to identify a minimally sufficient representation. The actual/fit/fore …

See my most recent post for an example of that ARIMA model has trouble forecasting next month . … Other posts illuminate how holiday effects can be automatically formed and you might also look at http://www.autobox.com/cms/index.php/afs-university/intro-to-forecasting/doc_download/53-capabilities-presentation …

incorporate price as a predictor variable . In this way the impact of the price variable will be accounted for in your forecast. Anomaly detection is also very important in forming a useful model. Bot …

"Typical time series models only depend on the time series data and not on external data." is not true unless you have no possible predictors. Time series analysis encompasses the utilization of both …

They are nearly synonyms ... although if you had a series like 1,2,1,1,1,2,2,1,1,3,1,2,1,1,2 .this might be better define as sparse but with no missing values i.e. '0's' thus it is not intermittent …

Exponential smoothing models are in general a subset of ARIMA models . When I say ARIMA models I am including the possibility of including trends, level shifts ,seasonal pulses and pulses in the equat …

https://stats.stackexchange.com/search?q=user%3A3382+daily+sales+data will give you a number of discussions regarding predicting daily data and subsequentally month end totals . The issue is you need …

With your data ... the mean would be the best model. However if one of the series was 280,290,300,310 .. my "best forecast would be 320 ... Additionally if the data was 100,80,100,80 , I would want t …