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I have a set of 50,000 time series lasting 31 years (each time series for each of 50,000 cells). I would like to find the probability that the next year will go to 0 given the values in previous years.

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If you assume that you actually can use previous data to infer future data, then: learn the statistical distribution and use it to predict the probability of a future value. A simple recipe would be to take all data and sort it into bins, with each bin index representing one of the possible values in that series, normalize the counted values (i.e. divide the count you got by the total amount of samples) and you get the probability for each of the values.

P.S. don't use this for the stock market :)

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  • $\begingroup$ Thank you! I don't think that would work, as the expectation is that the time series has an upwards trend. Also, I'm trying to retroactively determine the probability of already observed years using previous years (I would like to have probabilities for all years instead of observed data). For earlier years that would mean I would only have one or two years before that. $\endgroup$ – Katie Stevens Aug 22 '18 at 20:55
  • $\begingroup$ "For earlier years that would mean I would only have one or two years before that. " .. Can you please give a detaoiled example of that as I am confused by your words ... $\endgroup$ – IrishStat Aug 22 '18 at 21:43
  • $\begingroup$ if it's has an upwards trend then you can find the running mean and use it (add it) $\endgroup$ – Gomunkul Aug 22 '18 at 22:35
  • $\begingroup$ I have 31 years. I want to estimate the probability each year had of going to 0. So to estimate the probability of year 2 going to 0, I would only have the one data point for year 1. $\endgroup$ – Katie Stevens Aug 23 '18 at 12:11

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