Timeline for Difficult Time Series -Which forecast method?
Current License: CC BY-SA 4.0
4 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Aug 5, 2021 at 13:04 | comment | added | ykn frno | A colleague suggested the holt winters approach. But as I explained arima might not be working here due to the data being non stationary. | |
| Aug 5, 2021 at 12:34 | comment | added | ykn frno | I tried to model it with arima. But auto.arima in r run on error. Also acf showed high autocorrelation for more than 300 lags. The time series is not stationary and i am not sure how to make it so. Differencing might not be possible here | |
| Aug 4, 2021 at 12:41 | comment | added | Tylerr | What you suggest IS machine learning, it's simply using a machine to learn parameters although a lot of more traditional stats practitioners reserve it for non-parametric methods like trees or neural nets. Time series especially has been quite resistant to the term, see this excellent answer: stats.stackexchange.com/questions/160382/… | |
| Aug 4, 2021 at 11:41 | history | answered | confused student | CC BY-SA 4.0 |