Enhancing Forecast Accuracy for Damped Trend and Multiplicative Seasonality: A Hybrid Time-Series Framework with Simulation Study.

Forecasting time series characterized by a damped trend and multiplicative seasonality (DTMS) presents a significant challenge, as conventional models like ARIMA and ETS often fail to capture their complex, non-linear interactions. This study bridges this gap by developing and evaluating a novel hyb...

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Автори:	Md. Kamrul Hasan, Amrin Binte Ahmed, K. M. Zahidul Islam, Rumana Rois
Формат:	Online
Мова:	Англійська
Опубліковано:	Journal of Science 2026
Предмети:	Dhaka Stock Exchange, Damped trend and multiplicative seasonality, Hybrid machine learning, Low-volatility regime; Support Vector Regression.
Онлайн доступ:	http://journals.juniv.edu/index.php/jos/article/view/386
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