Abstract
We are to detect and identify the stochastic process of the large scale treering data series spanning more than 1000 years. We have applied the various time series analysing methods such as ARIMA and State Space Model, but estimation of transition probabilities as well as unknown state from the known information are explored. In addition we apply same model, if possible, by varying sample period to show how the result of identification from the sample is sensitive to the inference period and is different from each other. We found that large scale treering data series show abrupt changes in states, justifying the use of time series analyses allowing abrupt state changes. The length of sample and the choice of the time series analysis are the critical in making inference about the past behavior of the series and in making forecast in the future. Researchers need to pay special attention in choice of the methods consistent with the purpose of the studdy.
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