A Residual-Corrected Hybrid ARIMA–CNN–LSTM Framework for High-Accuracy Tobacco Sales Forecasting in Regulated Markets

• Published in: International journal of computational intelligence systems Vol. 18; no. 1; pp. 1 - 25
• Main Authors: Huang, Shiyu, Zhou, Lili
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 27.07.2025; Springer Nature B.V; Springer
• Subjects: Accuracy; ARIMA; Artificial Intelligence; Artificial neural networks; Autoregressive moving-average models; Computational Intelligence; Control; Deep learning; Engineering; Forecasting; Hybrid deep learning; Machine learning; Mathematical Logic and Foundations; Mechatronics; Optimization; Public health; Residual correction; Robotics; Sales; Time series analysis; Tobacco; Tobacco sales forecasting; Trend analysis; Hybrid deep learning; Time series analysis; ARIMA; Tobacco sales forecasting; Residual correction

As a common consumer product threatening public health, tobacco not only hinders the development of national public health, but also plays a significant impact on the national economy. The ARIMA model is reliable in learning linear or regular relationships, while the deep learn, such as convolutiona...