# How to predict the time series data

I have no background of advanced stats. I am an engineer and I have the following data. I am representing it as a decent graph for better understanding. I want to forecast the collision for the next hour with 95% confidence level in one hour interval.

The x-axis is the time (total 7 hours) and the Y-axis is the number of collisions (in thousands). The unit time is 1hr. This is a time series data and the process is stochastic. The nature is somewhat random as there is a trend clearly be seen but sometime abrupt movement can also happen. I was wondering if I get the idea where to start to get a initial prediction. I was reading the Bayesian time series analysis and then moved to ARIMA model but I am confused. So, any help. I would also want to know the real research issues in these kind of data. The tool I will be using is R or might be Matlab.

• Isn't y-axis the number of collisions? And what's the unit of time, e.g. that spike to 55k is 55k within what time interval? Commented Nov 26, 2014 at 16:17
• Sorry for the typos. I already fixed it. You can consider 2 rectangular blocks equal to 1 hr.
– Kal
Commented Nov 26, 2014 at 16:24
• As an engineer, you would appreciate Transfer function modeling which is a more general class of models than ARIMA. Try searching "Transfer Function Time Series" "Interrupted Time Series". Although, I have never seen it implemented in a low frequency series such as yours. Commented Nov 26, 2014 at 16:36
• @forecaster I am personally familiar with many successful applications of your suggested approach. Commented Nov 26, 2014 at 21:30
• @forecaster Actually this is the difference with the other approach. We can extend the time span (max of a week span) but we want it to predict it in hourly sense..
– Kal
Commented Nov 27, 2014 at 8:42