AN EFFICIENT HYBRID MACHINE LEARNING METHOD FOR TIME SERIES STOCK MARKET FORECASTING

O.M. Ebadati E. ； M. Mortazavi T.

《Neural Network World》 28卷1期 (2018/02) Pp. 41-55

https://doi.org/10.14311/NNW.2018.28.003

摘要

Time series forecasting, such as stock price prediction, is one of the most important complications in the financial area as data is unsteady and has noisy variables, which are affected by many factors. This study applies a hybrid method of Genetic Algorithm (GA) and Artificial Neural Network (ANN) technique to develop a method for predicting stock price and time series. In the GA method, the output values are further fed to a developed ANN algorithm to fix errors on exact point. The analysis suggests that the GA and ANN can increase the accuracy in fewer iterations. The analysis is conducted on the 200-day main index, as well as on five companies listed on the NASDAQ. By applying the proposed method to the Apple stocks dataset, based on a hybrid model of GA and Back Propagation (BP) algorithms, the proposed method reaches to 99.99% improvement in SSE and 90.66% in time improvement, in comparison to traditional methods. These results show the performances and the speed and the accuracy of the proposed approach.

關鍵字

time series forecasting ； stock price prediction ； genetic algorithm ； back propagation ； neural network ； machine learning