Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Akshithaa Parvathavardhini, K.P.; Chandra, J. Title A name given to the resource AI-Driven Time Series Models for Rainfall Prediction: A Machine Learning Approach Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE 14th International Conference on Communication Systems and Network Technologies, CSNT 2025;pp.42-46 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/CSNT64827.2025.10968081" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CSNT64827.2025.10968081</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105004555733?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105004555733?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Akshithaa Parvathavardhini K.P., Department of Computer Science Christ (Deemed to be University), Bangalore, India; Chandra J., Department of Computer Science Christ (Deemed to be University), Bangalore, India Description An account of the resource Rainfall variability plays a critical role in agriculture, water resources, and disaster management in India. This study focuses on the usage of a SARIMAX model to analyze and predict rainfall patterns while accounting for both non-seasonal and seasonal components. The model adequately captures seasonal variations, illustrating the relevance of incorporating seasonal factors into forecasting. However, the non-seasonal components did not considerably improve the models performance. Diagnostic tests demonstrate that the model handles autocorrelation satisfactorily; however, the residuals exhibit irregularities, as evidenced by skewness and large tails. While the model has fair prediction accuracy, the findings show areas for development, particularly in fine-tuning non-seasonal dynamics and eliminating residual abnormalities. This research emphasizes the importance of seasonality in rainfall forecasting and lays the framework for future model improvements. The study provides valuable information on rainfall patterns, supporting better planning and management. 2025 IEEE. Subject The topic of the resource ARIMA; Climate patterns; Rainfall variability; SARIMAX; Seasonal decomposition; Time series analysis Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833153193-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online