A Markov chain approach to the predictability of surface temperature over the northeastern part of India.

The present study reports a two-state Markov chain approach as well as an autoregressive approach to study the behavior of the surface temperature time series over northeast India. Considering the minimum requirement of the chi-square test, 1998–2007 (> 100 months) have been considered for testing the Markovian and autoregressive behavior. The monthly surface temperature time series involves monthly data that corresponds to a continuous random variable. This random variable has been discretized to binary data, and the transition probabilities have been computed up to the fourth-order Markov chain model. The best order of Markov chain has been derived through the minimization of the Bayesian information criterion (BIC). By analyzing the time series autocorrelation function and the Akaike information criteria (AIC), the autoregressive model of order two has been found to be a representative and the best autoregressive method for the average monthly time series of surface temperatures over northeast India. [ABSTRACT FROM AUTHOR]