Aug 2026· Applied and Computational Engineering· Vol 258, pp. 58-64· 0 citations
TL;DR
The study demonstrates that for taxi time series data with strong intraday cycles, the SARIMA model offers better prediction stability and accuracy, providing a reference for taxi capacity scheduling.
Abstract
Short-term taxi order forecasting is a key reference for dynamic allocation of urban transportation capacity. New York taxi passenger flow exhibits significant intraday cyclical fluctuations, making it difficult for manual dispatch to predict changes in passenger flow. This paper takes hourly taxi order time-series data in New York City as the research object, selecting the Seasonal Autoregressive Integrated Moving Average (SARIMA) model and the Support Vector Regression (SVR) model to conduct a short-term order prediction comparison. Based on AIC and BIC criteria, the optimal SARIMA parameters are traversed and screened, and SVR data are subjected to Min-Max normalization. Both types of models use multi-step rolling forecasting to generate a complete 7-day prediction sequence, relying on MAE, RMSE, MAPE, and R2 indicators to quantify prediction accuracy. Experimental results show that the optimal SARIMA (1,0,1)(1,1,24) model can accurately fit peak and valley fluctuations in passenger flow, with an R2 of 0.815. SVR models can only capture overall trends and lack the ability to characterize extreme passenger flows. The study demonstrates that for taxi time series data with strong intraday cycles, the SARIMA model offers better prediction stability and accuracy, providing a reference for taxi capacity scheduling.
Forecasting the number of train passengers is an important component in transportation system management to support operational planning and decision-making. Kediri Station, as a regional transit hub, exhibits highly dynamic daily passenger patterns with frequent fluctuations. The passenger time series data from this station shows occasional extreme spikes, particularly during holidays, long weekends, and promotional periods. These spikes can be characterized as Additive Outliers (AO), which may distort the underlying data structure and reduce the reliability of forecasting models if not properly addressed. This study aims to improve short-term forecasting accuracy of daily train passengers at Kediri Station by applying the Seasonal Autoregressive Integrated Moving Average (SARIMA) model with Additive Outlier (AO) handling. The analysis uses secondary daily passenger data from January 2024 to September 2025. AO effects are incorporated into the SARIMA framework to reduce bias caused by extreme observations. The results show that the SARIMA-AO model yields improved forecasting performance compared to the standard SARIMA model. The SARIMA-AO model achieves a Mean Squared Error (MSE) of 30,913 and a Mean Absolute Percentage Error (MAPE) of 13%, while the standard SARIMA model produces an MSE of 33,965 and a MAPE of 14%. Although the improvement is relatively modest, the results indicate that explicit handling of additive outliers can enhance forecast accuracy for daily passenger demand at Kediri Station. This approach provides preliminary evidence that AO-aware SARIMA modeling may be useful for supporting short-term operational planning in railway services.
K. M. Hindrayani, Nabila Lintang Ardani, Shindi Shella May Wara· bit-Tech· 0 citations
Accurate forecasting of air passenger demand is critical for aviation sector planning, yet model selection remains contested when data exhibit structural discontinuities and segment-specific behavior. This study benchmarks four forecasting approaches—SARIMA, SARIMAX, Prophet, and XGBoost—on monthly domestic and international passenger traffic data from 2019 to 2024, using a 12-month hold-out test set and a 36-month projection horizon. Model performance was evaluated using MAE, RMSE, and MAPE, with stationarity confirmed via Augmented Dickey-Fuller tests.Results reveal stark segmentation in model suitability. For international passengers, SARIMA achieved the lowest test MAPE (8.66%), substantially outperforming XGBoost (14.23%) and Prophet (57.65%), attributable to the strong first-order autocorrelation structure of post-recovery international traffic. For domestic passengers, SARIMA and SARIMAX both exhibited explosive forecast divergence (MAPE exceeding 11,900%), rendering them operationally unusable; XGBoost (MAPE 14.87%) emerged as the most reliable alternative by capturing non-linear interactions among lag features, rolling statistics, and annual trend components.The central finding concerns the role of exogenous variables: SARIMAX produced metrics identical to SARIMA for international passengers and marginally worse for domestic passengers, yielding no incremental predictive gain in either segment. This challenges the assumption that exogenous augmentation inherently improves seasonal time series models—such benefits are conditional on the relevance, stability, and non-redundancy of external regressors relative to existing seasonal components. These findings offer empirical guidance for segment-specific model selection in aviation demand forecasting and caution against uncritical adoption of SARIMAX when core seasonal structure is already sufficient.
Rianto Rianto, S. Mulyani, Setia Wardani· Vortex· 0 citations
Purpose: This study aims to compare the forecasting performance of the Autoregressive Integrated Moving Average (ARIMA) and Single Exponential Smoothing (SES) methods using Battery DYC spare-part demand data from PT XYZ as an industrial case study to identify the most appropriate forecasting approach for inventory planning.
Methods: Monthly sales data from January 2020 to May 2026 were analyzed using a quantitative time-series approach. The dataset was divided into training data (65 observations) and testing data (12 observations). The ARIMA model was developed according to the Box-Jenkins procedure, while the SES model used the optimal smoothing parameter estimated by SPSS. The forecast performance was assessed in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE).
Result/Findings: The results show that ARIMA (0,0,1) model has RMSE of 3.8370, MAE of 2.8925 and MAPE of 53.21%. SES (alpha = 0.052) has RMSE of 3.8336, MAE of 3.0000 and MAPE of 55.19%. SES is slightly better than ARIMA in terms of RMSE but ARIMA is better in MAE and MAPE, thus ARIMA is more robust overall.
Novelty/Originality/Value: The study provides empirical evidence from an industrial case study that forecasting performance is dependent on demand characteristics, rather than on the universal superiority of one method over another. The findings offer practical guidance for spare-part inventory planning and provide a basis for future comparisons with specialized intermittent demand forecasting methods.
Angger Styo Yuniarti, M. Kurniawan· Intechno Journal (Informatio...· 0 citations
Electricity demand forecasting is crucial for effective grid operation, planning, and decision-making. This study presents a comparison between classical time series models—AutoRegressive Integrated Moving Average (ARIMA) and Seasonal AutoRegressive Integrated Moving Average (SARIMA)—and a deep learning–based method, Gated Recurrent Units (GRU), for forecasting hourly electricity demand. The models are tested on a real-world dataset, enriched with weather and calendar variables to capture temporal and exogenous effects on electricity consumption. To ensure a fair and reproducible comparison, all models are trained and evaluated in a common experimental framework, including a well-defined chronological train-test split and rolling-origin (walk-forward) validation strategy. The forecasting performance is evaluated for short-term (24 h) and medium-term (168 h) horizons using standard error metrics, namely, root mean squared error, mean absolute error, and Mean Absolute Percentage Error (MAPE). The results of the experiment demonstrate that the GRU model performs better than ARIMA and SARIMA models especially for longer forecasting horizons due to its capability to learn nonlinear relationships and long-term temporal dependencies. The GRU approach gives better forecasting accuracy in the case of complex demand dynamics, but linear seasonal patterns can still be modeled by classical statistical models. The aim of this study is not to directly detect or predict system failures, nor does it depend on explicit fault or outage data. Its main contribution is instead in improving the accuracy of electricity demand forecasting, which can indirectly assist preventive grid operation and planning by reducing the uncertainty in expected load profiles.
Received: 7 August 2025 | Revised: 20 March 2026 | Accepted: 23 June 2026
Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.
Data Availability Statement
The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/saurabhshahane/electricity-load-forecasting.
Author Contribution Statement
Emrah Aslan: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Yıldırım Özüpak: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision. Feyyaz Alpsalaz: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision. Hasan Uzel: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision.
Emrah Aslan, Yıldırım Özüpak, Feyyaz Alpsalaz et al.· Artificial Intelligence and...· 0 citations
Smart grid activities demand a remedy to stability and economic problems and Short-term load forecasting (STLF) can provide some of the basic methods to deal with them. The traditional forecasting models that are found in literature flounder about localised and region specific data sets and therefore, their acceptability of results in general is always a demanding task without their strict cross validation. This current paper develops a distinctive solution with a multi city dataset that is extensive to determine the predictive quality of Artificial Neural Networks (ANN) and Linear Regression (LR) models. The test is carried out based on the high-resolution data on hourly data on NASA Earth data platform (n = 48,048) on January 2015 to June 2020, which includes national level data on electricity demand in Panama on the National Dispatch Center (CND) and local weather data. The proposed analytical framework has incorporated two-meter elevation variables such as temperature, humidity, wind speed and precipitation in three strategic region hubs of United States viz. Tocomen, Santiago and David. The model also takes into account exogenous temporal features, including the public holidays and the academic calendars to test the change in the socio-economic loads. The experimental findings indicate that Linear Regression (LR) model is better than the Artificial Neural Network (ANN) model because it has a small Root Mean Square Error (RMSE). This means that the underlying data set is more linear in nature and in this case, simpler models can be more effective than the more complex nonlinear models, where Linear Regression performs better than ANN in the current circumstances of the data set.
Shorya Mittal, N. Saxena, K. Gandhi et al.· Journal of Electrical System...· 0 citations
Very Short-Term Load Forecasting (VSTLF) is critical for real-time grid stability, frequency control, and economic dispatch. This study proposes a Gaussian Process Regression (GPR)-based framework for one-hour-ahead load forecasting using hourly data from January 2020 to April 2024 for Delhi, India. The model incorporates meteorological data such as temperature, humidity, and dew point with lagged load values. The research takes into account time-related dependencies and seasonal changes in order to boost the predictive power of the suggested model. Unlike deterministic neural models, GPR provides probabilistic predictions along with uncertainty quantification. Multiple kernel configurations were evaluated across datasets of increasing size (6,000–30,000 samples). The best- performing configuration (Exponential kernel, 25,000 samples) achieved a Testing RMSE of 111.43 MW, MAPE of 2.5349%, MAE of 77.99 MW, and R² of 0.9841. The evaluation highlights the model’s strength when faced with different data sizes and its capacity to deliver stable performance with little overfitting. Results demonstrate that GPR provides stable, accurate, and interpretable forecasting suitable for operational power system applications. The proposed framework presents substantial benefits regarding reliability, scalability, and adaptability for real-time implementation in contemporary smart grid settings, facilitating effective decision-making and enhanced energy management strategies. Adding uncertainty bounds to the mix bolsters operator confidence by facilitating planning that takes risk into account and management of the grid that anticipates problems.
Karan Sati, A. Yadav· Trends in Electrical Enginee...· 0 citations
