Timeline for Correlation with seasonal data
Current License: CC BY-SA 3.0
9 events
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| Jul 23, 2019 at 22:37 | history | edited | kjetil b halvorsen♦ |
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| Oct 4, 2018 at 9:51 | answer | added | kjetil b halvorsen♦ | timeline score: 1 | |
| Jul 31, 2017 at 13:52 | history | edited | kjetil b halvorsen♦ |
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| Jul 1, 2016 at 12:03 | comment | added | John Tarr | The data is proprietary, and I'm not sure how to add something representative of the amount of data I'm talking about. Aggregation is taking individual elements and combining them, as in, sum, average, etc. My understanding is that you can't just feed these decomposition functions all of the raw data, it must first be aggregated by month, quarter, or year. | |
| Jun 30, 2016 at 21:15 | comment | added | user78229 | "Data in aggregate?" What does this mean to you? Also, it would clarify your concerns if you were to add a sample of the data to your question. | |
| Jun 30, 2016 at 21:11 | comment | added | John Tarr | I really appreciate the comment, but the methods in R I've seen that decompose the data as you're describing seem to need the data in aggregate, which makes sense. I'm struggling with how to take that aggregate answer and apply it (if it is statistically valid to do so) back to the raw data. | |
| Jun 30, 2016 at 20:54 | comment | added | user78229 | The basic idea is that you want to understand the relationships over time in the absence of spurious relationships -- to the extent that this is statistically possible. There could be lots of issues lurking in your time series besides seasonality, e.g., autocorrelation, nonstationarity, trends, unit roots, and so on. Luckily, you have lots of data to work with. Look into removing these potential biases by developing "white noise" residuals and modeling that. One basic approach to this is a Holt-Winters decomposition of the data. | |
| Jun 30, 2016 at 20:42 | review | First posts | |||
| Jun 30, 2016 at 20:46 | |||||
| Jun 30, 2016 at 20:39 | history | asked | John Tarr | CC BY-SA 3.0 |