Application of PCA method to weather prediction task

A method of short-term weather forecasting based on artificial neural networks is presented. Each training sample consists of a date information combined with meteorological data from the last three days gathered at the meteorological station in Miami, USA. The prediction goal is the next day's temperature. Prediction system is built based on multilayer perceptron network trained with backpropagation algorithm with momentum. The average prediction error of the network on the test set equals 1.12/spl deg/C. The average percentage prediction error is equal to 5.72%. The results are very encouraging and provide a promise for further exploration of the issue. The so-called correlation ratio /spl delta/ between predicted and real changes (trends) is equal to 0.7136. Relatively high value of /spl delta/ additionally confirms good quality of presented results. Experimental results of application of the principal component analysis method at the stage of pre-processing of the input data are also presented. In that case the average prediction error and the average percentage prediction error are equal to 1.41/spl deg/C and 7.93%, respectively. In order to explain the reasons of the poorer results obtained with the PCA method a closer look at the principal components defined by the network is presented. Possible reasons of the PCA failure are pointed out.