In India, agriculture represents the primary occupation of more than 60% of the population. In terms of GDP, economic growth, traditional aspects, and social aspects, agriculture is essential for the country's development. The Indian farmers experienced numerous issues that have an impact on their way of life because the expansion in the agronomy business has not been as expected during the past two decades. Price fluctuation is one of the major issues faced by farmers, and as a result, they cannot get a reasonable price for their commodity. Also, it is very problematic to decide today without knowing the future price. So, this paper focused on finding a solution to the uncertainty problem in price faced by farmers that helps them take appropriate decisions during the farming process. The paper mainly concerns predictive data analytics using the ARIMA model, which predicts the price of areca nut products for the next 4 years using the past ten-year price dataset. The ARIMA model is a time series approach and a very appropriate framework for predicting future prices compared to other models. This paper includes a step-by-step procedure for the ARIMA techniques for forecasting price of agriculture commodity, and the outcomes are represented in the form of tables and graphical representations.
K. Vikranth, P.S. Nethravathi, K. Krishna Prasad
Srinivas University, India
ARIMA, Price Prediction, Time Series Approach, Areca Nut, Smart Agriculture
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 14 , Issue: 3 , Pages: 3304 - 3310 )
Date of Publication :
January 2024
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