Timeline for How to model a discontinuous Time Series with two or more "components"?
Current License: CC BY-SA 4.0
6 events
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| Sep 29 at 14:31 | comment | added | krkeane | I might be reading too much into your diagram, but I would describe the illustration as measurements from a sensor with two modes: functioning and broken. The data is generated from two distinct processes. I would look into a latent mixture model, where the mode switches with some probability given the mode in the prior time period. | |
| Sep 17 at 17:31 | vote | accept | James | ||
| Sep 7 at 20:22 | comment | added | zhaokg |
Not sure what is exactly meant by "forecasting". There are for sure lots of traditional methods for changepoint detection and time-series segmentation such as the strucchange, changepoint , and ecp packages in R and the ruptures package in Python. My own R and Python package Rbeast provides a Bayesian way, though no intended for forecasting, it can be twisted a little bit for prediction.
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| Aug 28 at 1:41 | answer | added | Adam Check | timeline score: 3 | |
| Aug 27 at 15:55 | answer | added | jmarkov | timeline score: 1 | |
| Aug 27 at 4:53 | history | asked | James | CC BY-SA 4.0 |