Timeline for Theoritical Question: How to create Self learning demand forecasting algorithm
Current License: CC BY-SA 3.0
19 events
| when toggle format | what | by | license | comment | |
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| Jan 6, 2016 at 4:04 | comment | added | StatguyUser | @ssdecontrol as per my understanding X-13 is equivalent of PROC X12 in SAS and it cannot work on daily data i.e. 365 data points for a year. It can work only at monthly aggregate i.e. 12 data points for a year or quarterly aggregate numbers i.e. 4 data point for a year. i tried running that for 365 data points in SAS and kept recieving error messages until i read SAS documentation support.sas.com/documentation/cdl/en/etsug/63348/HTML/default/… in detail which stated the limitation. Let me know if you think otherwise | |
| Dec 16, 2015 at 3:49 | answer | added | user91213 | timeline score: 2 | |
| Dec 15, 2015 at 4:01 | history | edited | StatguyUser |
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| Dec 14, 2015 at 16:30 | history | reopened |
shadowtalker gung - Reinstate Monica Sycorax♦ Silverfish Andy |
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| Dec 14, 2015 at 13:43 | history | edited | gung - Reinstate Monica | CC BY-SA 3.0 |
formatted; light editing
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| Dec 14, 2015 at 13:34 | comment | added | StatguyUser | Hi Tim, i just added that detail. | |
| Dec 14, 2015 at 13:34 | history | edited | StatguyUser | CC BY-SA 3.0 |
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| Dec 14, 2015 at 13:27 | review | Reopen votes | |||
| Dec 14, 2015 at 16:30 | |||||
| Dec 14, 2015 at 13:12 | comment | added | Tim | How does your data look like? Is it univariate or multivariate? What do you want to forecast? Please describe your data and your aims in greater detail. | |
| Dec 14, 2015 at 13:08 | history | edited | StatguyUser | CC BY-SA 3.0 |
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| Dec 13, 2015 at 16:25 | comment | added | shadowtalker | @MdAzimulHaque no, I don't mean "one size fits all." I mean "one size fits more than you might think." Take a look at Seasonal ARIMA models, and trend-cycle decomposition. The Census publishes a software package, X-13ARIMA-SEATS, devoted to seasonal adjustment | |
| Dec 13, 2015 at 15:47 | review | Reopen votes | |||
| Dec 13, 2015 at 19:12 | |||||
| Dec 13, 2015 at 15:40 | comment | added | StatguyUser | @ssdecontrol can you please elaborate on the second part of your second comment? can you provide me hint for published papers/books or further reading on how to build general models or modeling procedures, to handle seasonality and trends that vary between clients? If i understood correctly, there is some kind of 'one size fits all' kind of regression models? so once built, it can be used for multiple clients without any editing or re-modelling. | |
| Dec 13, 2015 at 15:33 | comment | added | shadowtalker | But I think you're also overthinking this problem, and probably also getting caught up by some buzzwords like "Deep Learning." You can build fairly general models or modeling procedures, without appealing specifically to machine learning, to handle seasonality and trends that vary between clients. | |
| Dec 13, 2015 at 15:29 | comment | added | shadowtalker | You might be interested in reinforcement learning and online learning. But this is an open area of research and as of yet we're a long long way away from having a drop-in general solution. Some companies claim to offer one, like Context Relevant, but they charge an arm and a leg to Fortune 500 companies; not something an average user can get their hands on. | |
| Dec 13, 2015 at 15:18 | history | closed |
Tim gung - Reinstate Monica whuber♦ |
Needs more focus | |
| Dec 13, 2015 at 13:49 | review | Close votes | |||
| Dec 13, 2015 at 15:18 | |||||
| Dec 13, 2015 at 13:31 | comment | added | Tim | Related stats.stackexchange.com/questions/160146/… , but in my opinion this question is too broad since it asks how to build whole system from the scratch and does not focus on any specific problem. | |
| Dec 13, 2015 at 13:20 | history | asked | StatguyUser | CC BY-SA 3.0 |