Title: Scheming a new algorithm for dynamic price prediction of vegetable commodities using statistical price prediction technique

Authors: R. Deepalakshmi; S. Padma Devi; J. Shanthalakshmi Revathy; T. Grace Shalini

Addresses: Department of Computer Science and Engineering, Velammal College of Engineering and Technology, Madurai-625009, Tamil Nadu, India ' Department of Computer Science and Engineering, Velammal College of Engineering and Technology, Madurai-625009, Tamil Nadu, India ' Department of Computer Science and Engineering, Velammal College of Engineering and Technology, Madurai-625009, Tamil Nadu, India ' Department of Computer Science and Engineering, Velammal College of Engineering and Technology, Madurai-625009, Tamil Nadu, India

Abstract: Agriculture is the back bone of our nation and plays major role in our nation. Data mining and big data plays a vital role in making a decision related to the agriculture field. The major drawback of using these algorithms for price prediction methodology is that only small sample of data set is taken for the processing and predicting the price of vegetable in one particular area. This paper proposes a new statistical price prediction (SPP) methodology to overcome this. The present study was undertaken with the objectives to build appropriate forecasting model and to forecast the groundnut price of Madurai market of Tamil Nadu. SPP is used along with the statistical measurement and calculation for the unknown sample of data and a time series analysis is made for the appropriate price prediction of sample data collected. This technique provides a view for estimating the price of vegetable in future and provides a possible way to analyses the price.

Keywords: agriculture; data mining; machine learning algorithm; time series analysis; price prediction algorithm.

DOI: 10.1504/IJCCIA.2019.103753

International Journal of Computational Complexity and Intelligent Algorithms, 2019 Vol.1 No.2, pp.117 - 128

Received: 08 Mar 2018
Accepted: 22 Jun 2018
Published online: 26 Nov 2019 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article