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Time Series Forecasting with Hybrid SARIMA

Publication Type : Journal Article

Publisher : IEEE

Source : 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS)

Url : https://doi.org/10.1109/icmlas64557.2025.10967979

Campus : Coimbatore

School : School of Physical Sciences

Year : 2025

Abstract : Forecasting is an important task in any organization so that they can predict the targets and modify their strategies in order to improve their productivity of sales for the upcoming future. This paper primarily deals about the prediction of products for sales. The focus has been mainly on forecasting techniques like SARIMA and it is optimized using a gradient boosting algorithm to improve the prediction rate. The dataset that has been used for current predictions is a source from Kaggle. The data comprises of both linear and non-linear data to simulate the real time sales.

Cite this Research Publication : Pramothini S, Aakash P, Vanmathi VM, Somasundaram K, Senthil Kumar Thangavel, Time Series Forecasting with Hybrid SARIMA, 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS), IEEE, 2025, https://doi.org/10.1109/icmlas64557.2025.10967979

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