designing a model for financial forecasting using the integration of neural networks with the box-jenkins and holt-winters methodologies

The present study designs a model for financial forecasting by integrating neural networks. this retrospective comparative study uses the average price data of opec oil from 2003 to 2022 to forecast the period from june 2022 to may 2024. to this end, two time-series models (box-jenkins and holt-winters) were examined, which in the second stage were incorporated into a hybrid model based on artificial neural networks. the neural network model was developed using matlab, and the box-jenkins time-series model was constructed using spss and eviews software. based on the results of the error analysis of the box-jenkins methodology, among the time series processes arima(5,1,5), arima(4,1,5), arima(3,1,5), and arima(5,1,3), the models demonstrated the best accuracy with mse values of 61.86, 63.21, 63.29, and 63.62, respectively. the accuracy of the holt-winters method was not suitable for time-series forecasting due to the nature of the data. therefore, the best artificial neural network was designed for combining forecasting methods. this neural network included an input layer with 5 neurons, a hidden layer with 5 neurons, and a single-neuron output layer. the network was trained using the levenberg-marquardt algorithm and employed a linear sigmoid activation function. the results indicated that the designed hybrid neural network significantly improved the accuracy of the forecasting methods and enhanced the mse, mape, aic, and bic indices.