I have more than 400 different events that occur during two years, some of them can occur 4000 times an others no more than 50 times. These events are not equally distributed, e.g., one at 15:00:02, other at 15:10:45 and other at 15:45:56. I cannot ensure that these events are independents, they maybe can be relationed. I want to analyze these events and try to find a pattern of events.
Type of data:
{timestamp1, event A (string value)}, {tiemstamp2, event B (string value)} ->Between timestamp1 and timestamp2 there is no event and is not equally distributed. Event A could inflluence event B or not.
I would like to know what type of methodology I can apply. I have been reading about SAX time series, Markov model, Hidden Markov model, DTW (Dynamic Time Warping), Time wrap for discrete events, Renewal proccess model and continous-time Markov process. However I think that these algorithms don't fit to my problem.
I hope that someone can help me. Thanks in advaced.
I do not know what kind of time-series you are working on but I would try access its spectrum; because your observations are not regularly sampled you can try to
"fit" a Fourier series.
Hope it helps. Good luck.
Thanks. I have events (categorical variable - Event A, ..., Event N) ordered in time, each event has its own timestamp. I just to find patterns in this type of time series, for example, {Event A -> Event C -> Event G -> Event D}
If the data is different in distribution, a spectrum analysis of the data can be adopted using the spectrum density function, or a consistent estimate of the density density function or the periodiagram function.
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