Timeline for How to predict sales with only sporadic, occasional, marketing costs in time series?
Current License: CC BY-SA 3.0
11 events
| when toggle format | what | by | license | comment | |
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| Oct 12, 2016 at 3:43 | comment | added | GeoMatt22 | I just posted an answer summarizing these comments. Hope it helps! | |
| Oct 12, 2016 at 3:43 | answer | added | GeoMatt22 | timeline score: 1 | |
| Oct 12, 2016 at 3:17 | comment | added | python novice | Just read the full article, thanks for this. never thought my signal processing course will be this helpful. Thank you. | |
| Oct 12, 2016 at 3:13 | history | edited | GeoMatt22 | CC BY-SA 3.0 |
typo in title
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| Oct 12, 2016 at 3:12 | comment | added | GeoMatt22 | I was not certain of the question context, and a lot of econometric time-series questions seem to focus on stationarity and differencing/integrating time series to achieve this. So I crudely meant "integrate" in the sense of "smoothing" the response (vs. differencing, which tends to "roughen"). Really more the idea of a lagged & distributed response though, as in the impulse response link I gave. | |
| S Oct 12, 2016 at 3:02 | history | suggested | Carl | CC BY-SA 3.0 |
tag, spelling gammar sentence structure.
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| Oct 12, 2016 at 2:50 | comment | added | python novice | Yup, that kind of data, like the pays for ads. I am not sure what you meant with integrated form, though. | |
| Oct 12, 2016 at 2:31 | comment | added | GeoMatt22 | I would imagine that marketing expenditures are for distributed types of results, e.g. pay for an ad that then runs for days or weeks? I am not sure if you need stationarity to do prediction. Some sort of "integrated form" of the marketing costs could be used perhaps? For example, a convolution with an "impulse response" kernel? | |
| Oct 12, 2016 at 1:24 | review | Suggested edits | |||
| S Oct 12, 2016 at 3:02 | |||||
| Oct 12, 2016 at 1:15 | review | First posts | |||
| Oct 12, 2016 at 2:18 | |||||
| Oct 12, 2016 at 1:09 | history | asked | python novice | CC BY-SA 3.0 |