ISSN 2320-5407 International Journal of Advanced Research (2016), Volume 3, Issue 535-541 535 Journal homepage: http://www.journalijar.com INTERNATIONAL JOURNAL OF ADVANCED RESEARCH RESEARCH ARTICLE FORECASTING VEGETABLE PRICE USING TIME SERIES DATA M.Subhasree 1 , Mrs.C.Arun Priya 2 . 1. M.Phil Research Scholar, Department of computer science. Psgr krishnammal college for women, Coimbatore, Tamil Nadu. 2. Assistant Professor, Department of computer science, Psgr krishnammal college for women, Coimbatore, Tamil Nadu. Manuscript Info Abstract Manuscript History: Received: 14 December 2015 Final Accepted: 19 January 2016 Published Online: February 2016 Key words: Back propagation neural network, genetic algorithm, radial basis function. *Corresponding Author M.Subhasree. Predicting the vegetable price is essential in agriculture sector for effective decision making. This forecasting task is quite difficult. Neural network is self-adapt and has excellent learning capability and used to solve variety of tasks that are intricate. This model is used to predict the next day price of vegetable using the previous price of time series data. The three machine learning algorithms are incorporated in this work namely Radial basis function, back propagation neural network and genetic based neural network are compared. The models are assessed and it is concluded from the derived accuracy that the performance of genetic based neural network is better than back propagation neural network and radial basis function and improves the accuracy percentage of vegetable price prediction. Copy Right, IJAR, 2016,. All rights reserved. Introduction:- a. Existing system:- Vegetable price changes fast and unstable which makes great impact in our daily life. So, it is hard to predict the vegetable price. Based on the complexity of vegetable price prediction, making use of the characteristics of data mining classification technique like neural networks such as self adapt, self-study and high fault tolerance, to build up the model of back propagation neural networks to predict vegetable price. Back Propagation Neural Network is usually based on the error back propagation to the multi-layer Neural Network. In this system, former three week data of tomato price are taken as input and later one week data as output for weekly price prediction. So three input neurons for weekly price prediction consider. Three layer feed forward network structure is used for weekly vegetable price prediction. b. Proposed system:- In the proposed work, vegetable price prediction using time series data is carried out by using genetic based neural network. The proposed work consists of four steps  Normalization  Network structure creation  Training  Testing c. Data Collection and Data Preparation:- Vegetable prices are affected by several factors such as climate, supply, demand, and festival etc. so the prediction is more difficult than ordinary commercial products. It is very difficult to collect data based on these factors. Therefore in this work, taken only the most perishable vegetables Tomato, Ladies finger, Broad beans, Small onion and Brinjal as experimental data. Most important point in network design is determining the data size and frequency. This is mostly depends on the final output. Taking previous three weeks daily price of five vegetable is taken for simulating