Timeline for time series decomposition/dtrending using splines
Current License: CC BY-SA 3.0
10 events
| when toggle format | what | by | license | comment | |
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| Sep 15, 2013 at 7:34 | comment | added | Rob Hyndman | Please read what I wrote. "If the data are non-seasonal, just use any nonparametric smoothing method to estimate trend." | |
| Sep 13, 2013 at 16:11 | vote | accept | forecaster | ||
| Sep 13, 2013 at 14:20 | history | edited | forecaster | CC BY-SA 3.0 |
added 45 characters in body
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| Sep 13, 2013 at 14:19 | comment | added | forecaster | Can we extract trend from a non seasonal time series data using STL, Census (X-13-ARIMA) and classical decomposition ?. I thought all these require seasonal component i.e., frequency > 1. | |
| Sep 13, 2013 at 2:51 | comment | added | Rob Hyndman | OK. But there are still errors. You can extract the trend using STL, Census (X-13-ARIMA) and classical decomposition. If the data are non-seasonal, just use any nonparametric smoothing method to estimate trend. | |
| Sep 13, 2013 at 1:14 | answer | added | Wayne | timeline score: 1 | |
| Sep 13, 2013 at 0:38 | comment | added | forecaster | Rob, I have modified the question. I deleted the incorrect statement. | |
| Sep 13, 2013 at 0:34 | history | edited | forecaster | CC BY-SA 3.0 |
I have deleted previous incorrect statement that STL is NOT a data driven decomposition method.
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| Sep 12, 2013 at 22:29 | comment | added | Rob Hyndman | What's not "data driven" about STL? It is fully nonparametric. | |
| Sep 12, 2013 at 21:56 | history | asked | forecaster | CC BY-SA 3.0 |