Prediction of Indian petroleum product prices by using Kalman Filter based Neural Networks

Abstract

The experiments are performed over the petroleum product prices data which are downloaded from Indian oil corporation Limited (IOCL) official website. These data are updated on daily basis according to the guidelines from Indian government and the price of international market. Predictions of PP prices are sound interesting as the fluctuations in the PP prices are influencing the stock market prices. We have compared Kalman filter based neural networks (KFNN) with two other well-known neural networks (NNs) training method for the petrol price predictions. Results show that the KFNN training method is converging faster and shows better performance with less error. Therefore KFNN showed effectiveness for PP price predictions.