Comparative study of wavelet-ARIMA and wavelet-ANN models for temperature time series data in northeastern Bangladesh

Time-series analyses of temperature data are important for investigating temperature variation and predicting temperature change. Here, Mann-Kendall (M-K) analyses of temperature time-series data in northeastern Bangladesh indicated increasing trends (Sen's slope of maximum and minimum yearly temperature at Sylhet of 0.03 degrees C and 0.026 degrees C, respectively, and a minimum temperature at Sreemangal of 0.024 degrees C) except for the maximum temperature at Sreemangal. The linear trends showed that the maximum temperature is increasing by 2.97 degrees C and 0.59 degrees C per hundred years, and the minimum, by 2.17 degrees C and 2.73 degrees C per hundred years at the Sylhet and Sreemangal stations, indicating that climate change is affecting temperature in this area. This paper presents an alternative method for temperature prediction by combining the wavelet technique with an autoregressive integrated moving average (ARIMA) model and an artificial neural network (ANN) applied to monthly maximum and minimum temperature data. The data are divided into a training dataset (1957-2000) to construct the models and a testing dataset (2001-2012) to estimate their performance. The calibration and validation performance of the models is evaluated statistically, and the relative performance based on the predictive capability of out-of-sample forecasts is assessed. The results indicate that the wavelet-ARIMA model is more effective than the wavelet-ANN model. (C) 2015 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.