Attention-Based Novel Deep Learning Framework for Mango Price Prediction Using Time Series Data

Mango holds a significant place globally. Considering its importance, it is also called 2 the king of fruits. Accurate price forecasting is essential for market decisions, policy 3 formulation, and agricultural market stability. Traditional time series models struggle 4 to provide effective and accurate forecasts of Mango prices and cannot capture their 5 nonlinear dynamics. The current study integrates machine learning, deep learning, and 6 statistical models to build a robust forecasting model using a dataset from 2001 to 2025. This 7 study proposed a novel attention-based Mango price forecasting approach. It significantly 8 forecasted Mango prices in the Indian market. It combines the strengths of various models 9 and produces generalized results. The hybrid ETS + ANN + GARCH model has high 10 predictive accuracy (MAE = 0.0498, MSE = 0.0106, RMSE = 0.1028, R2 209 = 0.774) and 11 ETS+SVM Hybrid achieves the accuracy level (MAE = 0.063, MSE = 0.006, RMSE = 0.078, 12 R2 = 0.873). The performance of ETS + BiLSTM is also significant, with an accuracy level 13 97.5%. Thus, an attention-based approach offers a new technological paradigm for mango 14 price forecasting.