A new time series prediction algorithm based on moving average of nth-order difference

As a typical research topic, time series analysis and prediction face a continuously rising interest and have been widely applied in various domains. Current approaches focus on a large number of data collections, using mathematics, statistics and artificial intelligence methods, to process and make a prediction on the next most probable value. This paper proposes a new algorithm using moving average of nth-order difference to predict the next term for pseudo- periodical time series. We use artificial neural networks (ANNs) and range evaluation for error in a hybrid model to extend our prediction method further. The algorithm performances are reported on case studies on monthly average sunspot number data set and earthquake data set.