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Price forecasting & anomaly detection for agricultural commodities in India

Published: 03 July 2019 Publication History

Abstract

Fluctuations in food prices can cause distress among both consumers and producers, and are often exacerbated by trading networks especially in developing economies where marketplaces may not be operating under conditions of perfect competition for various contextual reasons. We look at onion and potato trading in India and present the evaluation of a price forecasting model, and an anomaly detection and classification system to identify incidents of hoarding of stock by the traders. Our dataset is composed of time series of wholesale prices and arrival volumes of the agricultural commodities at several village-level marketplaces, and retail prices of the commodities at the city centers. We also provide an in-depth qualitative analysis of the effect on these time series of events such as hoarding, weather disturbances, and external shocks. Our results are encouraging and point towards the possibility of building pricing models for agricultural commodities which can be used to reduce information asymmetries and to detect anomalies that can help regulate agricultural markets to operate more fairly.

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cover image ACM Conferences
COMPASS '19: Proceedings of the 2nd ACM SIGCAS Conference on Computing and Sustainable Societies
July 2019
290 pages
ISBN:9781450367141
DOI:10.1145/3314344
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 03 July 2019

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Author Tags

  1. agriculture
  2. analysis
  3. anomaly
  4. commodities
  5. prices
  6. time series

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  • (2023)Deep-Learning-Based Price Prediction by Outlier Detection and Processing for Agricultural Commodity PricesJournal of Digital Contents Society10.9728/dcs.2023.24.8.189924:8(1899-1906)Online publication date: 31-Aug-2023
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