Abstract—Weather prediction is a challenging task for researchers and has drawn a lot of research interest in the recent years. Literature studies have shown that machine learning techniques achieved better performance than traditional statistical methods. This paper presents an application of Support Vector Machines (SVMs) for weather prediction. Time series data of daily maximum temperature at a location is analyzed to predict the maximum temperature of the next day at that location based on the daily maximum temperatures for a span of previous n days referred to as order of the input. Performance of the system is observed over various spans of 2 to 10 days by using optimal values of the kernel function. Non linear regression method is found to be suitable to train the SVM for this application. The results are compared with Multi Layer Perceptron (MLP) trained with back-propagation algorithm and the performance of SVM is found to be consistently better.
Index Terms—Comparative Studies, Neural Networks, Regression, Time Series, Weather Forecasting
Y. Radhika is with the Computer Science Engineering Department, GITAM University, Visakhapatnam, Andhra Pradesh INDIA. (Ph: 91-9985225454)
M. Shashi is with the Computer Science and Systems Engineering Department, Andhra University, Visakhapatnam, Andhra Pradesh INDIA. (Ph: 91-9949072880).
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Cite: Y.Radhika and M.Shashi, "Atmospheric Temperature Prediction using Support Vector Machines,"
International Journal of Computer Theory and Engineering vol. 1, no. 1, pp. 55-58, 2009.
