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...

Celý popis

Podrobná bibliografie

Hlavní autoři:	Md. Kamrul Hasan, Amrin Binte Ahmed, K. M. Zahidul Islam, Rumana Rois
Médium:	Online
Jazyk:	angličtina
Vydáno:	Journal of Science 2026
Témata:	Dhaka Stock Exchange, Damped trend and multiplicative seasonality, Hybrid machine learning, Low-volatility regime; Support Vector Regression.
On-line přístup:	http://journals.juniv.edu/index.php/jos/article/view/386
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!