Version 1
: Received: 18 July 2024 / Approved: 18 July 2024 / Online: 18 July 2024 (14:27:50 CEST)
How to cite:
Shah, V.; Patel, N.; Shah, D. A.; Swain, D.; Mohanty, M.; Acharya, B.; Gerogiannis, V. C.; Kanavos, A. Forecasting Temperature Trends Using SARIMAX: A Case Study in Ahmedabad City, India. Preprints2024, 2024071521. https://doi.org/10.20944/preprints202407.1521.v1
Shah, V.; Patel, N.; Shah, D. A.; Swain, D.; Mohanty, M.; Acharya, B.; Gerogiannis, V. C.; Kanavos, A. Forecasting Temperature Trends Using SARIMAX: A Case Study in Ahmedabad City, India. Preprints 2024, 2024071521. https://doi.org/10.20944/preprints202407.1521.v1
Shah, V.; Patel, N.; Shah, D. A.; Swain, D.; Mohanty, M.; Acharya, B.; Gerogiannis, V. C.; Kanavos, A. Forecasting Temperature Trends Using SARIMAX: A Case Study in Ahmedabad City, India. Preprints2024, 2024071521. https://doi.org/10.20944/preprints202407.1521.v1
APA Style
Shah, V., Patel, N., Shah, D. A., Swain, D., Mohanty, M., Acharya, B., Gerogiannis, V. C., & Kanavos, A. (2024). Forecasting Temperature Trends Using SARIMAX: A Case Study in Ahmedabad City, India. Preprints. https://doi.org/10.20944/preprints202407.1521.v1
Chicago/Turabian Style
Shah, V., Vassilis C. Gerogiannis and Andreas Kanavos. 2024 "Forecasting Temperature Trends Using SARIMAX: A Case Study in Ahmedabad City, India" Preprints. https://doi.org/10.20944/preprints202407.1521.v1
Abstract
Globalization and industrialization have significantly disturbed the environmental ecosystem, leading to critical challenges such as global warming, extreme weather events, and water scarcity. Forecasting temperature trends is crucial for enhancing the resilience and quality of life in smart sustainable cities, enabling informed decision-making and proactive urban planning. This research specifically targets Ahmedabad city in India and employs the Seasonal Auto Regressive Integrated Moving Average with eXogenous factors (SARIMAX) model to forecast temperatures over a ten-year horizon using two decades of real-time temperature data. The stationarity of the dataset was confirmed using the Augmented Dickey-Fuller test, and the Akaike Information Criterion (AIC) method helped identify the optimal seasonal parameters of the model, ensuring a balance between fidelity and predictive accuracy. The model achieved an RMSE of 1.0265, indicating high accuracy within the typical range for urban temperature forecasting. This robust measure of error underscores the model’s precision in predicting temperature deviations, which is particularly relevant for urban planning and environmental management. The findings provide city planners and policymakers with valuable insights and tools for preempting adverse environmental impacts, marking a significant step towards operational efficiency and enhanced governance in future smart urban ecosystems. Future work may extend the model's applicability to broader geographical areas and incorporate additional environmental variables to refine predictive accuracy further.
Keywords
temperature forecasting; weather forecasting; time series; augmented Dickey-Fuller test; seasonal autoregressive integrated moving average with eXogenous factors (SARIMAX); root mean squared error; seasonality; climate change
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
