This software, OPFor (Oil Price Forecast) system, is developed mainly for oil price prediction including long term and short term.
      For long term prediction, it bases on a special forecasting model for future oil price. The model applies pattern matching technique to multi-step prediction of crude oil prices and proposes a new approach: generalized pattern matching based on genetic algorithm (GPMGA), which can be used to forecast future crude oil price based on historical observations. This software can automatically detect the most similar pattern in contemporary crude oil prices from the historical data. Based on the similar historical pattern, a multi-step prediction of future crude oil prices can be figured out. In GPMGA modeling process, the traditional pattern matching is not directly employed. Historical data is transformed to larger or smaller scales in the x-axis and the y-axis directions, so that a generalized price pattern reflecting current price movement can be obtained. This treatment overcomes the local deficiency of the traditional pattern modeling in recognition system approach (PMRS), and in addition to this, a matched historical pattern in a larger pattern size can be found. Since the approach takes not only historical similarities but also differences into account, the concept of “generalized pattern matching” is proposed here. It proves a new basis for multi-step prediction by finding out more essential similarities through various transformations.
     For short term prediction, the wavelet technique is applied. First, oil price is decomposed into time series of different frequencies. Then, these series are approximated separately by sin function. Finally, the forecast result is obtained combining above approximations.
      In addition, this software is designed to perform some basic tasks for the processing of oil price data. For example, it consists of a database of SQL to store all kinds of oil price and is able to transform the daily data into yearly or monthly data, some basic statistical function realized as well.
      Six components are included in OPFor whose framework Fig.1 illustrates.
      Several screen snapshots are shown in Fig.2 which is the login interface of OPFor and Fig.3 when OPFor is plotting.

Fig.1 The framework of OPFor

Fig.2 The login view of OPFor

Fig.3 The running mode of OPFor