Integration of Hybrid ARIMA Artificial Neural Networks for Accurate Platinum Price Prediction

Abstract

Introduction: The autoregressive integrated moving average (ARIMA) has been a widely used linear model in time series forecasting for the last thirty years. Furthermore, as a potent and adaptable computational tool, artificial neural networks (ANNs) have been employed in recent years to capture the intricate economic interactions with a range of patterns. Efficacy of the ANNs model in comparison to the ARIMA model, the majority of this research has shown inconsistent findings.

Objectives: The study aims to increase the accuracy of platinum price forecasts through the use of hybrid models, ANNs, and ARIMA (Auto Regressive Integrated Moving Average) techniques. ARIMA is a traditional time series model that is used to capture and model the intrinsic oscillations in platinum price data.

Methods: In this research work, we present a hybrid model that combines the best features of ANNs and ARIMA to simulate both linear and nonlinear behaviors in the data set. According to the study, the Hybrid model outperforms the Box-Jenkins and FFNN models in terms of forecasting different data sets with more accuracy. Here study investigates the developing field of platinum price prediction with a comprehensive approach that blends state-of-the-art machine learning methods with traditional time series analysis.

Results: In order to increase forecasting accuracy, combining single and hybrid models has more possibilities. In order to forecast time series, this study compared ARIMA, ANN, and hybrid models. Results of this study reveals that Hybrid model is 66% and 90% better model than ARIMA and FFNN models respectively and empirical results from the application of Hybrid model reduces 50% error metrics in out of sample  in comparison to in sample Therefore the suitable model for prediction of Platinum prices is Hybrid model. Using hybrid models to increase predicting accuracy yielded the most significant findings.

Conclusions: A better time-series forecast is essential, but there are several areas of emphasis for the created forecasting models, particularly with regard to these improvements in prediction accuracies that support predictions. They are often used to linear and nonlinear forecasting models. Even while single models are correct during some prediction periods, hybrid models often provide superior overall prediction outcomes than single forecasting models.