EDIT: added more details following @kjetil comments
I have the following problem:
- I monitor one stream of events of type A - those events can be considered instantaneous.
- I also monitor additional streams of events of types B1, B2,... Bn - those have a time interval associated with them.
- I want to determine if the frequency of type A events increases significantly during any specific B types (in other words, I want to determine exactly which B types have a significant positive influence during the intervals of their events)
- Many instances of the same type of event can occur simultaneously
- The effect is likely to be linear in the number of concurrent events of the specific B type
- There are probably many B types that don't effect A-events
- B types of events are not necessarily the only thing that influence type A events
- All events probably have some complex distribution that changes depending on the day/hour/week/month/previous events - not a simple Poisson
- I constantly monitor all streams - I want to discover when a B-type starts to influence the frequency of A-event and I would also want to know if at some point in the future it stopped influence it (and this should continue indefinitely))
- A-type events are power outages
- B1-type events correspond to opening and closing of air conditioners.
- B2-type events correspond to opening and closing of televisions.
Do you have any idea how I should approach this problem? (general references are fine)