- My data goes up and down regularly and can take both negative and positive values.
- Using Dickey-Fuller Test, the time series is shown to be stationary with p-value of 0.0000 and t-statistic of -7.1999. So, to my knowledge, differencing is not needed in this case.
Now I'm trying to determine the order using ACF and PACF plots. Grid search is unfortunately not feasible on my computer since the data is quite large (over 20.000 hourly data points). However, I got following results:
There is something unusual from the results. In both plots, the correlations never go below the significant level. This means that every lag can be chosen as a order. Furthermore, it's often said that the seasonal order of SARIMA is equal to the ACF lag with the highest value, which in this case refers to lag 1. This does not quite make any sense since my data is hourly data.
Can anyone advise on these following questions:
- How to determine the order from these plots?
- Is this data set appropriate for (S)ARIMA model? If not, will transforming the data, e.g. log transformation or differencing, help?
- Is there any other time series model recommended for this dataset?