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

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

Abstract 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 con...

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Bibliographic Details
Main Authors: Shiyu Huang, Lili Zhou
Format: Article
Language:English
Published: Springer 2025-07-01
Series:International Journal of Computational Intelligence Systems
Subjects:
Hybrid deep learning
ARIMA
Tobacco sales forecasting
Residual correction
Time series analysis
Online Access:https://doi.org/10.1007/s44196-025-00930-4
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https://doi.org/10.1007/s44196-025-00930-4

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