Stock prices are not stationary processes during all week or all day. For example EURGBP has low variability at night in Europe but during working hours is changing much more dynamic because of market liquidity.
I want to collect history data (15 minutes interval), calculate ARIMA coefficients and get prediction in R. But it is sensless to include data from night hours if I trade only during day.
So, is it possible to create ARIMA model based on discontinous data series (like 10:00 - 16:00 Monday, 10:00 - 16:00 Tuesday, 10:00 - 16:00 Wednesday, etc.)? How to merge this data minimizing the error (price from Tuesday 10:00 de facto is not next price after Monday 16:00)?