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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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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