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Apr 13, 2017 at 12:44 history edited CommunityBot
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Jul 25, 2015 at 12:13 comment added IrishStat @forecaster On second thought,why don't you pose a question regarding just one of the two series under discussion.Present the data and your model and detail how it was how it was identified using a list-based procedure.Present the normal statistics for the model including tests of parameter significance and a complete analysis of the errors validating your "white noise" conclusion. I will respond using methods and procedures which you perhaps don't have access to and compare/contrast your model and the one I suggested. This might be educational and I think it will be of interest to the list.
Jul 25, 2015 at 7:15 comment added IrishStat Parsimony is an objective BUT model sufficiency is a higher objective. Take your model compute the errors and then take those errors and examine them for sufficiency with Intervention detection or simply plot the errors.
Jul 25, 2015 at 3:14 comment added forecaster @IrishStat, I disagree with the proposed model in the link, I would pick random walk with drift as it is more parsimonious than the proposed model from Autobox.
Jul 24, 2015 at 1:45 comment added IrishStat stats.stackexchange.com/questions/161571/… discusses two types of trends
Jul 24, 2015 at 1:20 comment added forecaster @IrishStat I'm not sure if I'm following trend changes 1,2,3,... ? is it local trend ?
Jul 23, 2015 at 22:17 comment added IrishStat @forecaster That may be true but I would be further interested in performing/reporting head-to-head comparison in terms of variance reduction between the two approaches. Did you consider incorporating deterministic trend changes involiving 1,2,3,....type series or was this not part of the study ?.
Jul 23, 2015 at 21:56 comment added forecaster @Irishstat, I figured out a way by which we can check deterministic features first and then use tsoutliers package and it works just fine.
Jul 23, 2015 at 10:47 comment added IrishStat Unfortunately the free R package has some serious shortcomings . It assumes that the dominant structure is memory by identifying the ARIMA portion first whereas the deterministic portion may be dominant. Secondly and perhaps more importantly it uses a naive one step list-based procedure (AIC/BIC) to identify the ARIMA portion from a fixed set of models whereas ARIMA model is an iterative identification process.
Jul 23, 2015 at 5:09 comment added lovekesh @IrishStat Thanks for the links. R package looks great. I will go through the module.
Jul 22, 2015 at 21:41 comment added IrishStat this is a non-starter "This article presents a control chart for time series data, based on the one-stepahead forecast errors of the Holt-Winters forecasting method." as it is specific model presumptive forecasting method
Jul 22, 2015 at 21:25 comment added user603 Have you searched the literature on robust control charts? You could start here
Jul 22, 2015 at 18:36 comment added IrishStat @whuber sorry about that ... I have expanded my answer ....
Jul 22, 2015 at 18:35 history edited IrishStat CC BY-SA 3.0
added 229 characters in body
Jul 22, 2015 at 16:19 comment added whuber Both links go to the same answer of yours, which is quite general and does not clearly address outlier detection at all. Since you have posted often, and sometimes in detail, about outlier detection in time series, I'm confident you could find a better reference than that!
Jul 22, 2015 at 16:09 comment added IrishStat @adam which link are you referring to ? All my links refer to time series as that is the only subject/topic I know .
Jul 22, 2015 at 16:04 comment added adam Question is related to outlier detection in time series, your link does not provide any information on this
Jul 18, 2015 at 19:59 history answered IrishStat CC BY-SA 3.0