A lightweight time-series analysis model through multi-teacher knowledge distillation for food price forecasting

Abstract

Accurate food price forecasting is critical for food security planning, particularly in developing nations like Bangladesh where price volatility can significantly im-pact vulnerable populations; however, existing deep learning approaches for time series forecasting often require substantial computational resources, limiting their deployment in resource-constrained environments. This study presents a novel multi-teacher knowledge distillation framework that transfers knowledge from an ensem- ble of three distinct teacher architectures—DLinear, PatchTST, and N-BEATS— trained on World Food Programme (WFP) Bangladesh commodity price data from the Dhaka Division to compact student models (MLP, GRU, KAN) through a multi- component distillation loss comprising prediction-level matching, feature-level alignment, and price-difference learning, with an uncertainty-weighted mechanism that focuses training on confident teacher predictions while dynamically weighting teacher contributions based on validation performance. Experimental evaluation on four food commodities (Lentils, Oil, Rice, and Wheat flour) with a 6-month input window demonstrates that the proposed approach achieves a Mean Absolute Error (MAE) of 1.959 BDT/unit with a Mean Absolute Percentage Error (MAPE) of only 3.73%, representing a 37% improvement over the supervised learning baseline, 69% improvement over traditional ARIMA, and 81% improvement over LSTM baselines, with the three-teacher ensemble distilled to an MLP student achieving the best results and outperforming all single-teacher and two-teacher configurations. The resulting student model requires only 200K parameters (compared to over 1M in the teacher ensemble) and achieves inference in sub-millisecond time on standard CPU hardware without GPU acceleration, enabling deployment in humanitarian field offices with limited computational infrastructure. This study contributes a reproducible, configuration-driven framework for knowledge distillation in time series forecasting, demonstrating that sophisticated ensemble-level accuracy can be achieved with lightweight models suitable for resource-constrained field deployment in food security applications.