Abstract:
Analyzing and predicting time-series data is a fundamental and widely used technique in economics. For instance, predicting monthly product sales figures can be influence...View moreMetadata
Abstract:
Analyzing and predicting time-series data is a fundamental and widely used technique in economics. For instance, predicting monthly product sales figures can be influenced by various external factors, but analyzing historical data can uncover significant patterns such as trends and seasonality. To achieve precise predictions, advanced models have been proposed in the literature. Moreover, combining different models can enhance the accuracy of predictions. In this paper, we propose a nonlinear combination of models to create a more accurate time-series model. While we focus on two primary categories of models, namely the Prophet model and the ARIMA model, our proposed method is not limited to these two models.
Published in: 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT)
Date of Conference: 03-06 July 2023
Date Added to IEEE Xplore: 24 October 2023
ISBN Information: