The current research reports a univariate analysis of 2 important climatological parameters surface air temperature and rainfall over North East India over annual scale characterized by various degrees of non-linearity. An Autocorrelation study reveals that although the surface air temperature is characterized by an approximate sinusoidal pattern, the rainfall has no apparent pattern. As both the time series have continuous random variables associated with it we need a discretization to make them suitable for study in time domain. First, we test for Markovian behavior against serial independence. By appropriate discretization of both the time series the transition probabilities are computed to test for Two State Markov Chain model with different orders. A chi square test reveals that although the surface air temperature time series follows the Markov Chain model of 1st Order, the annual rainfall is serially independent. The steady state transition probabilities are computed for surface air temperature and subsequently autoregressive models upto order 4 with the help of Yule-Walker Equations. It was further observed that the 2nd order model satisfied the stationarity conditions. Subsequently to have a general overview of the stationarity, Unit Root Test is carried out for both the time series. For surface air temperature a root was found to exist inside the circumference of the unit circle while for rainfall time series finds a root of the characteristic equation outside unit circle. This indicates stationarity of the surface air temperature time series for its 1st order differences and non-stationarity of rainfall time series. Based on this, Autoregressive Integrated Moving Average (ARIMA(p,q,d)) model is fitted to the rainfall time series for various orders of Autoregression and differencing. Subsequently the performance of ARIMA has been compared to univariate artificial neural network model with 4 successive realizations as predictor and the 5th one as predictand. In order to test the relative contributions to these meteorological phenomenon, maximization of Shannon entropy is used.

目前的研究报告对印度东北部的两个重要气候参数，地表温度和年降水量，以非线性程度为特征进行了单变量分析。自相关研究表明，尽管地面气温的特征是近似正弦曲线，但降雨没有明显的规律。由于两个时间序列都具有连续的随机变量，因此我们需要进行离散化处理，以使其适合于时域研究。首先，我们针对序列独立性测试马尔可夫行为。通过对两个时间序列进行适当的离散化，可以计算出转移概率，以测试具有不同阶数的两种状态马尔可夫链模型。卡方检验表明，尽管地表气温时间序列遵循一阶马尔可夫链模型，但年降水量却是连续的。借助Yule-Walker方程计算出地面空气温度的稳态转变概率，然后计算出自回归模型直至4级。进一步观察到二阶模型满足平稳性条件。为了对平稳性有一个总体了解，对两个时间序列都进行了单位根检验。对于表面空气温度，发现根存在于单位圆的圆周内，而对于降雨时间序列，发现特征方程的根在单位圆之外。这表明地面空气温度时间序列的一阶差分的平稳性和降雨时间序列的非平稳性。基于此，将自回归综合移动平均值（ARIMA（p，q，d））模型拟合到降雨时间序列中，以实现自回归和微分的各种阶数。随后，将ARIMA的性能与单变量人工神经网络模型进行比较，以4个连续实现作为预测变量，第5个连续实现作为预测变量。为了检验对这些气象现象的相对贡献，使用了香农熵的最大化。随后，将ARIMA的性能与单变量人工神经网络模型进行比较，以4个连续实现作为预测变量，第5个连续实现作为预测变量。为了检验对这些气象现象的相对贡献，使用了香农熵的最大化。随后，将ARIMA的性能与单变量人工神经网络模型进行比较，以4个连续实现作为预测变量，第5个连续实现作为预测变量。为了检验对这些气象现象的相对贡献，使用了香农熵的最大化。