Timeline for How would you fit ARIMA model with lots of autocorrelations?
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Apr 30, 2012 at 21:17 | comment | added | IrishStat | @Dimitri We have just developed this enhanced feature within AUTOBOX , a software package that I helped to develop. Our customer a large fast food franchise will not allow us to publish the results as they don't want their competitors to be able to work/plan smarter. I can tell you that the system allows for price/promotion/holidays etc to play a role in both the 96 individual forecasts and the daily forecast. We are using promotional inputs as predictor series. If you want them to demo this for you please contact them at [email protected] | |
| Apr 30, 2012 at 15:21 | comment | added | dimitriy | Do you have a reference with an example of this type of model? I am particularly interested in how you deal with price(s) and what the exogenous variables are. | |
| Apr 29, 2012 at 19:59 | history | edited | IrishStat | CC BY-SA 3.0 |
added 818 characters in body
|
| Apr 29, 2012 at 19:54 | comment | added | IrishStat | @Luna As you correctly point out one loses the connection between the different time slices BUT one gains the impact of activity over days/weeks/months while being able to detect changes in daily effects , while discovering the impact of particualr days of the month etc.. We like you had studied the "one-time series approach" using semi-hourly electricity demand data only to conclude that we were getting FALSE CONCLUSIONS due to the size/length of the data. In general one could have 96 equations with X eXogenous series . This would be called a VeEctor ARIMA | |
| Apr 29, 2012 at 18:46 | comment | added | Luna | Thank you Dave! So you have 5 years of 9:00am data, 5 years of 9:15am data, 5 years of 9:30am data,... these are all daily time series, and you fit these daily time series (there are 96 of them)? this approach is interesting, but then you neglect the connections between the 9:00am and the 9:15m and the 9:30am data points, no? Is there a way to do, as you suggested, the separate model fitting for 96 of your daily time series, and in the meantime, model the interrelation among these 96 models? Thank you! | |
| Apr 29, 2012 at 12:28 | history | answered | IrishStat | CC BY-SA 3.0 |