An ensemble stacking framework for machine learning and statistical models to improve forecasts in dynamic demand and shows that stacking can capture heterogeneous demand dynamics better than single models and, hence, provide a more robust structure.
Historical sales information was the primary predictive signal, while weather provided supplementary information, and an interpretable forecasting framework for daily-location sales revenue can support revenue-oriented forecasting and help assess the additional value of incorporating weather information.
Muhammad Shaheryar, Monzer Alharairi, S. H. Amin et al.· Logistics· 1 citation
Sales forecasting remains a cornerstone of effective retail management, guiding supply chain
optimization, inventory planning, and revenue strategies. Traditional forecasting models often fall
short in capturing the complexity of consumer behavior, seasonal fluctuations, and branch-level
variations, resulting in ineffi...
Mercy Doowuese Anza· International Journal of Com...· 0 citations
Accurate retail sales forecasting is essential for effective inventory management, efficient supply chain operations, and informed strategic business planning. Although machine learning techniques have significantly improved forecasting accuracy, their limited interpretability often reduces user confidence and restrict...
S. Mohankumar, S. Kiruthika, N. Raghapriya et al.· 2026 6th International Confe...· 0 citations
A comparative forecasting framework for weekly VIX prediction by integrating statistical and machine learning models with Bayesian optimization for hyperparameter tuning is proposed and indicates that persistence-based statistical models outperform more complex machine learning models under the fixed holdout evaluation...
Abstract This study investigates the influence of input window size and ensemble size on the forecasting performance of Decision Tree-based ensemble methods in one-step-ahead financial return prediction. Nineteen financial instruments spanning stocks, indices, commodities, and cryptocurrencies are evaluated under a chr...
Eddy Suprihadi, Nevi Danila, G. Ananta et al.· Open Computer Science· 0 citations
This study compares econometric, linear regression, and machine-learning models for forecasting five-day close-to-close volatility across 12 diversified US exchange-traded funds. The dataset covers 2,511 trading days from September 2016 to August 2026 and includes equities, bonds, precious metals, commodities, and real...
Suganya Eliot, Muthulakshmi Angamuthu· Applied Data Science and Ana...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
