A Comparison of Different Weather Forcasting Models
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Date
2017
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Publisher
University of Management and Technology
Abstract
In this thesis, we studied the performance of different statistical models and compare their forecast accuracy. In particular, we used multiple linear regression (MLR), seasonal autoregressive fractional integrated moving average (SARFIMA), and artificial neural network (ANN). A dynamic non-linear autoregressive (NAR) back-propagation ANN algorithm has been applied to estimate the forecast accuracy. For ANN model, we used moving average (MA) and Holt-Winter exponential smoothing (HW-ES) transformations for pre-processing the data. The monthly data of different weather parameters have been obtained from the Lahore Metrological department, Pakistanto apply the aforementioned models. The results showed that the ANN model with MA transformation of the data has the smallest root mean square error and the highest correlation coefficient for different weather parameters. Thus, ANN outperforms than the rest models in this study and it can be used to efficiently forecast the weather parameters.
Description
Supervised by: Dr. Muhammad Moeen Butt
Keywords
Forecast accuracy, Artificial neural network, MS Thesis