I am new to time series modeling and am trying to come up with a solution for a problem. My problem is about estimation of next value in a time series, which is made of four components. One is picked randomly from 3 different gamma distributions. One follows a uniform distribution and the other follows a Weibull distribution.
My simple query is, that if there is hope for such a data to use time series modeling and forecasting of future data. I am asking just a guidance on whether I should go through this path or find some other alternative solution to my problem, by not using prediction at all.
Is it worth investing time in learning topics like ARMA/ARIMA/GARCH etc. in this scenario?
Edit: Adding one more important piece of information which I missed. I will explain this with an example. Imagine that I have a series of cups coming toward me, and I fill them with variable amount of water. The amount of water is filled like this:
- Pick one value randomly from one of the 4 gamma distributions with different $\alpha$ and $\beta$ parameters.
- Some of the cups need special attention. Those cups are selected using a Weibull distribution which is fixed. If a particular cup is marked as special, then more water is filled, and this quantity is picked from an exponential distribution.
- The above step is considered again simultaneously with same distributions but with different parameters.
So I have to predict how much water will fall into a new cup which comes to me. The cups are coming toward me and the water is filled in each from these 3 sources. Can I use time series modeling here?
EDIT: If time series modeling is not good here, please give suggestions about other feasible methods!