Advice Sought on Time Series Forecasting for Agricultural Crop Volumes and Prices
A data professional at a major berry company is seeking community advice on machine learning-based time series forecasting for agricultural applications. Their role requires forecasting weekly crop harvest volumes and future commodity pricing using USDA and industry datasets. They have experimented with SARIMA, XGBoost, and Holt-Winters models and are now looking for production-grade libraries, suitable model architectures, and feature engineering strategies that incorporate weather, seasonality, acreage, and imports. The data is weekly and exhibits strong seasonality, with weather and supply shocks as key drivers. The post solicits recommendations on frameworks, papers, and practical lessons for forecasting in agriculture.