A Comprehensive Spatio-Temporal Dataset for Agricultural Price Forecasting: Integrated Market, Weather, and Event Features (2014–2024)

This dataset presents a comprehensive, all-India spatio-temporal compilation of onion market information integrated with high-resolution climate and event-based features covering the period 2014–2024. The dataset was fully assembled from publicly available government sources, including Agmarknet for daily market arrivals and prices, and the India Meteorological Department (IMD) for gridded rainfall and temperature data. Each record represents a unique combination of state, district, market, variety, and date, enabling fine-grained agricultural market analysis across India.

To capture weather–market interactions, the dataset includes daily rainfall, Tmax, Tmin, derived and rolling climatic features, and long-window climatic signals relevant to onion growth cycles. Additional event-level variables such as festivals, elections, and supply-disruption periods are included to model non-seasonal volatility. All features were carefully engineered with strict anti–data-leakage protocols, ensuring suitability for machine learning, deep learning, and econometric forecasting tasks.

The resulting dataset is designed for research in time-series forecasting, price volatility analysis, supply chain modeling, climate–market interactions, and agricultural risk assessment. It serves as a clean, ready-to-use resource for academic, industrial, and policy-oriented studies on Indian agricultural markets.