A Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX) Forecasting Model-Based Time Series Approach

Abstract

Time series modeling is an effective approach for studying and analyzing the future performance of the power sector based on historical data. This study proposes a forecasting framework that applies a seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) model to forecast the long-term performance of the electricity sector (electricity consumption, generation, peak load, and installed capacity). In this study, the model was used to forecast the aforementioned factors in Saudi Arabia for 30 years from 2021 to 2050. The historical data that were inputted into the model were collected from Saudi Arabia at quarterly intervals across a 40-year period (1980−2020). The SARIMAX technique applies a time series approach with seasonal and exogenous influencing factors, which helps reduce the error values and improve the overall model accuracy, even in the case of close input and output dataset lengths. The experimental findings indicated that the SARIMAX model has promising performance in terms of categorization and consideration, as it has significantly improved forecasting accuracy compared with the simpler autoregressive integrated moving average-based techniques. Furthermore, the model is capable of coping with different-sized sequential datasets. Finally, the model aims to help address the issue of a lack of future planning and analyses of power performance and intermittency, and it provides a reliable forecasting technique, which is a prerequisite for modern energy systems.

Prediction of Inbound and Outbound Passenger Flow in Urban Rail Transit Based on Spatio-Temporal Attention Residual Network

“…Following the same steps as above, we can construct three spatio-temporal modules of closeness, period, and trend in Figure 4. After processing through the model, the outputs of the three modules are X As the influence of closeness, period, and trend varies across different sites, we adopt a parametric-matric-based fusion method [39] to merge the three spatio-temporal modules and obtain the output X RAN : (10) where W c , W p , and W q are weight parameters representing the degree of influence of three modules, respectively, and • indicates the Hadamard product.…”

“…The autoregressive integrated moving average model(ARIMA) [9], as described, combines three steps: the autoregressive, integrated, and moving average, which can flexibly adapt to different time series data and obtain the optimal prediction results by adjusting parameters. Seasonal autoregressive integrated moving average (SARIMA) [10] is an expansion of the ARIMA model which captures seasonal effects by adding additional seasonal differences and seasonal autoregressive terms. However, both require complex parameter selection for the model, and they perform poorly when there are multiple seasonal patterns in the time series.…”

Predictive analytics for inventory optimization

“…The study underlines SARIMAX's ability to outperform complex alternatives, indicating its versatility and adaptability across diverse industries. Moreover, the dynamic nature of SARIMAX, as highlighted by Arunraj et al (2016) and Alharbi & Csala (2022), enables the model to effectively account for unforeseen changes in future demand, thus enhancing the accuracy and dependability of sales forecasts. The model's capability to integrate external variables and capture the impact of various dynamic factors contributes to its robustness and reliability in addressing forecasting challenges.…”

Enhanced S‐ARIMAX model performance and state‐of‐health prediction accuracy with battery pack degradation factor combinations

“…The artificial neural network (ANN) approach is adept at predicting rapidly changing or distinctly periodic data. However, when addressing long‐term trends or patterns, combining ANN with other models might be beneficial, considering factors such as extensive data learning, data quality, and prolonged interactions 24 . Deep learning techniques like CNN and RNN secure high‐precision SOH predictions through automated feature extraction algorithms embedded within the model.…”

“…However, when addressing long-term trends or patterns, combining ANN with other models might be beneficial, considering factors such as extensive data learning, data quality, and prolonged interactions. 24 Deep learning techniques like CNN and RNN secure high-precision SOH predictions through automated feature extraction algorithms embedded within the model. Yet, this approach demands substantial training data, high computing power, and may generate features lacking direct physical interpretation, limiting its effectiveness across batteries with varying characteristics.…”