I'm using R(3.1.1), and ARIMA models for forecasting.
I would like to know what should be the "frequency" parameter, which is assigned in the
ts() function, if im using time series data which is:
- separated by minutes and is spread over 180 days (1440 minutes/day)
- separated by seconds and is spread over 180 days (86,400 seconds/day).
If I recall right the definition, a "frequency" in ts in R, is the number of observations per "season".
Question part 1:
What is the "season" in my case?
If the season is "day", then is the "frequency" for minutes = 1440 and 86,400 for seconds?
Question part 2:
Could the "frequency" also depend on what I am trying to achieve/forecast? for example, in my case, I'd like to have a very short-term forecast. One-step ahead of 10minutes each time. Would it then be possible to consider the season as an hour instead of a day? In that case frequency= 60 for minutes, frequency = 3600 for seconds?
I've tried for example to use frequency = 60 for the minute data and got better results compared to frequency = 1440 (used
fourier see link below by Hyndman)
(The comparison was made by using MAPE for the measure of forecast accuracy)
In case the results are complete arbitrary, and the frequency cannot be changed. What would be actually the interpretation of using freq = 60 on my data?
I also think it's worth mentioning that my data contains seasonality at every hour and every two hours (by observing the raw data and the Autocorrelation function)