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Short Term Forecasting of Agriculture Commodity Price by Using ARIMA: Based on Indian Market

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Advances in Computing and Data Sciences (ICACDS 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1045))

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Abstract

The Forecasting of agriculture commodity price plays an important role in the developing country like India, whose major population directly or indirectly depends upon farming. There are several forecasting techniques like Time series analysis, regression techniques, learning techniques. We used Auto Regressive Integrated Moving Average (ARIMA) model under Time series analysis for forecasting, which consider only the historical data. We selected price of sunflower seed for the period 1st January 2011 to 31st December 2016, gathered from “data.gov.in” for the market Kadiri, Anantpur district, Andhra Pradesh, India. We used the data from 1st Jan, 2011 to 31st Dec 2015 for training purpose and the data from 1st Jan, 2016 to 31st Dec 2016 for testing purpose. Based on the training data, ARIMA(1, 1, 2) selected as best model. Mean Average Percentage Error (MAPE) for the selected model is calculated as 2.30%. The Root Mean Square Percentage Error (RMSPE) observed by the model as 3.44%.

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Authors and Affiliations

  1. Department of CSE, Jamia Hamdard, Hamdard Nagar, New Delhi, India

    Anil KumarMahto, Ranjit Biswas & M. Afshar Alam

Authors
  1. Anil KumarMahto
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  2. Ranjit Biswas
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  3. M. Afshar Alam
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Corresponding author

Correspondence to Anil KumarMahto .

Editor information

Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Computer Science and Engineering, Jaypee Institute of Information Technology, Waknaghat, Himachal Pradesh, India

    P.K. Gupta

  3. Department of Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Vipin Tyagi

  4. ÚTIA AV ČR, Institute of Information Theory and Automation, Prague 8, Praha, Czech Republic

    Jan Flusser

  5. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

  6. CSE Department, Inderprastha Engineering College, Ghaziabad, Uttar Pradesh, India

    Rekha Kashyap

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KumarMahto, A., Biswas, R., Alam, M.A. (2019). Short Term Forecasting of Agriculture Commodity Price by Using ARIMA: Based on Indian Market. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Kashyap, R. (eds) Advances in Computing and Data Sciences. ICACDS 2019. Communications in Computer and Information Science, vol 1045. Springer, Singapore. https://doi.org/10.1007/978-981-13-9939-8_40

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  • DOI: https://doi.org/10.1007/978-981-13-9939-8_40

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  • Online ISBN: 978-981-13-9939-8

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