Univariate modelling of monthly maximum temperature time series over northeast India: neural network versus Yule-Walker equation based approach

作者：Chattopadhyay Surajit^*; Jhajharia Deepak; Chattopadhyay Goutami

来源：Meteorological Applications, 2011, 18(1): 70-82.

DOI：10.1002/met.211

• autocorrelation
• autoregressive model
• stationary
• trend
• Yule-Walker equation
• autoregressive neural network
• modular neural network

The present paper has adopted an autoregressive approach to inspect the time series of monthly maximum temperature (T-max) over northeast India. Through autocorrelation analysis the T-max time series of northeast India is identified as non-stationary, with a seasonality of 12 months, and it is also found to show an increasing trend by using both parametric and non-parametric methods. The autoregressive models of the reduced T-max time series, which has become stationary on removal of the seasonal and the trend components from the original time series, were generated through Yule-Walker equations. The sixth order autoregressive model (AR(6)) is identified as a suitable representative of the T-max time series based on the Akaike information criteria, and the prediction potential of AR(6) is also established statistically through Willmott's indices. Subsequently, autoregressive neural network models were generated as a multilayer perceptron, a generalized feed forward neural network and a modular neural network. An autoregressive neural network model of order four (AR-NN(4)), in the form of a modular neural network (MNN), has performed comparably well with that of AR(6) based on the high values of Willmott's indices and the low values of the prediction error. Therefore, AR-NN(4)-MNN will be a better option than AR(6) to forecast a time series, i.e. the monthly T-max time series of northeast India, because AR-NN(4)-MNN requires fewer predictors for a superior forecast of a time series.

• 出版日期2011-3