Deep learning-enabled cherry price forecasting and real-time system deployment across multi-market supply chains in India.

ABSTRACT: For high-value perishable goods such as cherries, accurate price forecasting is crucial for the supply chain, stabilizing producer income, informing policy interventions & real-time decision support at various stakeholder levels. This study enquires whether Deep Learning (DL) architectures can be implemented in real time for daily advisory tools while simultaneously surpassing conventional statistical and Machine Learning (ML) models in predicting cherry prices. To tackle this issue, daily cherry price data from five wholesale markets in India were utilized for the period 2012-2024 to evaluate six forecasting methodologies, i.e., Seasonal Auto-regressive Integrated Moving Average (SARIMA), Prophet, Random Forest (RF), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and

SUBMITTER: Shaheen FA 

PROVIDER: S-EPMC12800224 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature