I am beginner in forecasting, especially forecasting with R and I am really willing to improve my knowledge.
Recently, I started practicing electricity consumption time series forecasting.
The first barrier I faced is the choice of out of sample data for assessing the forecast accuracy of the forecast model i will be using (regression with ARIMA errors).
I have data for 147 months and I want to forecast the next 24 months, for the period June 2013 to January 2015. Furthermore, I have read in @RobHyndman's online text book
Hyndman, R.J. and Athanasopoulos, G. (2013),
Forecasting: principles and practice,
(accessed 28 May 2013), section 2.5
under 'Training and test sets', that:
size of the test set is typically about 20% of the total sample, although this value depends on how long the sample is and how far ahead you want to forecast
If I divide the dataset with 20% of it being the out-of-sample data, the forecast model applied to the in-sample data is not quite accurate, since I guess it fails to capture the recent trend, (which began in the middle of the last year), of decreased electricity consumption due to a significantly raised electricity tariff.
What do I do about this?
Can you possibly give me instruction on what would be considered appropriate size of the out of sample data. I also tried with 7 months of out-of-sample data, but I am afraid that there might be an overfitting issue. Is that right?