Timeline for Is stl a good technique for forecasting, instead of Arima?
Current License: CC BY-SA 3.0
9 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jul 2, 2019 at 2:58 | comment | added | user11284 | @Tim You can use the trend and seasonal component together to generate a point forecast. Treat the remainder series as prediction error. | |
| Aug 28, 2017 at 11:43 | vote | accept | Kumar Manglam | ||
| S Aug 18, 2017 at 14:45 | history | suggested | Ferdi | CC BY-SA 3.0 |
Adding two tags and reformatting in body
|
| Aug 18, 2017 at 14:30 | review | Suggested edits | |||
| S Aug 18, 2017 at 14:45 | |||||
| Aug 18, 2017 at 12:03 | answer | added | Ferdi | timeline score: 15 | |
| Aug 18, 2017 at 10:18 | comment | added | forecaster | Stl is not a forecasting technique, STL helps you decompose time series data into seasonal, trend and mouse components. STL as it is implemented in R does not have the ability to handle multiple seasonalities which means it is not suitable for handling hourly data. | |
| Aug 18, 2017 at 7:57 | comment | added | AlexR | Exponential smoothing state space models can make use of STL trend data and can be used to predict time series by adding the last season of seasonal data. Maybe this is what you want? | |
| Aug 18, 2017 at 7:53 | comment | added | Tim | How exactly would you like to use STL for forecasting..? STL is a method for decomposing your data into the three components (i.e. exploratory data analysis and visualization) not for making any predictions. | |
| Aug 18, 2017 at 7:51 | history | asked | Kumar Manglam | CC BY-SA 3.0 |