Time Series Forecasting for Agriculture/Crop Volume & Pricing – Looking for Advice [D](reddit.com)

Hi everyone, I work for a major berry company, and a large part of my role involves forecasting total industry crop volumes (weekly harvest/production forecasts) as well as future pricing. I'm relatively new to ML-based forecasting. This is only my second professional role, and I have a bachelor's degree in Information Systems with a few machine learning courses under my belt, but I'm definitely not a forecasting expert. For crop forecasting, I've been working with USDA and other industry datasets. I started with SARIMA models and have recently been experimenting with XGBoost and Holt-Winters methods to compare performance. I'm looking for recommendations on: Libraries/frameworks that are commonly used for production-grade time series forecasting Models that work well for agricultural production forecasting Approaches for forecasting commodity/produce pricing Feature engineering ideas (weather, seasonality, acreage, imports, etc.) Any papers, blogs, or resources that would be useful Most of the data is weekly and highly seasonal, with weather and supply conditions playing a major role. Any suggestions, lessons learned, or pointers from people working in forecasting would be greatly appreciated. submitted by /u/foreigneverythingg [link] [Kommentare]