Feature extraction and interval filtering technique for time-series forecasting using neural networks

This paper presents the algorithm for feature extraction and interval filtering technique for time-series forecasting using multilayer perceptron neural networks. The algorithm has four parts. The first part is data filtering and interval process. The second part is input feature extraction process from neural networks. The third part is time-series input variables forecasting process. The fourth part is time-series rainfall forecast process. The study uses weather data from the Meteorological Department of Thailand and the United States of America. The experimental results for rainfall forecast receive high accuracy comparing with other methods.