Differential Autoregressive of Cycle Prediction Model and its Application

Abstract:

AR model is widely used which based on stationary time series used for short-term prediction. However, in fact the time series we got is often non-stationary, and there is little literature researching the smooth processing, modeling and forecasting and then restoring the results in system. In view of this, this paper provides a method, that is, differential autoregressive of cycle prediction. First, explain the basic principles and give the calculation steps of smooth processing, modeling and forecasting and restoring the results. Then, applied the prediction method in the short-term forecast of coal arrive of a provincial. Model implementation is based on java programming. We get high prediction accuracy, the system easily integrated, can be widely used, and can achieve rolling forecast.

• Periodical: Applied Mechanics and Materials (Volumes 198-199)
• Pages: 1315-1320
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.198-199.1315
• Online since: September 2012
• Authors: Cai Lian Luo, Huan Qi, Su Qin Sun
• Keywords: Autoregressive Model, Cycle Forecast, Differential Treatment, Prediction

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