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Accuracy of crop price forecasting techniques is important because it enablesthe supply chain planners and government bodies to take appropriate actions byestimating market factors such as demand and supply. In emerging economies suchas India, the crop prices at marketplaces are manually entered every day, whichcan be prone to human-induced errors like the entry of incorrect data or entryof no data for many days. In addition to such human prone errors, thefluctuations in the prices itself make the creation of stable and robustforecasting solution a challenging task. Considering such complexities in cropprice forecasting, in this paper, we present techniques to build robust cropprice prediction models considering various features such as (i) historicalprice and market arrival quantity of crops, (ii) historical weather data thatinfluence crop production and transportation, (iii) data quality-relatedfeatures obtained by performing statistical analysis. We additionally propose aframework for context-based model selection and retraining considering factorssuch as model stability, data quality metrics, and trend analysis of cropprices. To show the efficacy of the proposed approach, we show experimentalresults on two crops - Tomato and Maize for 14 marketplaces in India anddemonstrate that the proposed approach not only improves accuracy metricssignificantly when compared against the standard forecasting techniques butalso provides robust models.

A Framework for Crop Price Forecasting in Emerging Economies by Analyzing the Quality of Time-series Data