ArticleNeural computing & applications2023
Demand forecasting model for time-series pharmaceutical data using shallow and deep neural network model.
Article in Neural computing & applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- A hybrid grey wolf optimized eXtreme gradient boosting-based machine learning model for hospital pharmaceutical demand forecasting.Scientific reports · 2026Article
- Knowledge graph-enhanced deep learning for pharmaceutical demand forecasting.Scientific reports · 2026Article
- Hybrid machine learning forecasting for resilient and sustainable pharmaceutical supply chains under regulatory and seasonal disruption.Frontiers in artificial intelligence · 2026Article
- Uncovering the Probiotic Supplement Landscape: Market Offerings, Sales Patterns, and Future Forecasts Using Machine Learning Approach - A Case Study of Montenegro.Probiotics and antimicrobial proteins · 2025Article
- Long-term forecast for antibacterial drug consumption in Germany using ARIMA models.Naunyn-Schmiedeberg's archives of pharmacology · 2025Article
- A Decision Probability Transformation Method Based on the Neural Network.Entropy (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
Demand forecasting is a scientific and methodical assessment of future demand for a critical product.The effective Demand Forecast Model (DFM) enables pharmaceutical companies to be successful in the global market. The purpose of this research paper is to validate various shallow and deep neural network methods for demand forecasting, with the aim of recommending sales and marketing strategies based on the trend/seasonal effects of eight different groups of pharmaceutical products with different characteristics. The root mean squared error (RMSE) is used as the predictive accuracy of DFMs. This study also found that the mean RMSE value of the shallow neural network-based DFMs was 6.27 for all drug categories, which was lower than deep neural network models. According to the findings, DFMs based on shallow neural networks can effectively estimate future demand for pharmaceutical products.
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Registered trials
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