trend forecasting in financial time series using a combinational method of heuristic pattern recognition and support vector machine

Whereas many studies have been done on forecasting various time series, it has always been associated with challenges such as uncertainty. for example, in financial time series, due to the time series non-stationary feature, forecasting is likely to encounter false regression. to solve this problem, in this research, trend forecasting has been done instead of price forecasting. in this case, since the subtraction operator has been used to calculate the trend, the effect of the non-stationary feature is removed and the issue of false regression is solved. to achieve this aim, the trend in financial time series has been predicted using machine learning methods. in this research, the effective features of the last 10 years in the commodity stock market data for the shares of several various companies have been examined and compared with the benchmark index of the market. after creating different machine learning models and maximizing the accuracy of the results, a satisfying application has been extracted to be used as an effective trading tool for traders. the random forests algorithm and support vector machine, feature selection, and heuristic algorithms have been used to train the model. the achieved results show that the proposed model is capable of producing accurate forecasts, and also outperforms other approaches currently in use.