Price Forecasting on a Large Scale Data Set using Time Series and Neural Network Models

• Preetha K G,
• K R Remesh Babu,
• Sangeetha U,
• Rinta Susan Thomas,
• Saigopika,
• Shalon Walter,
• Swapna Thomas,

Vol. 16, No. 12, December 31, 2022
10.3837/tiis.2022.12.008, Download Paper (Free):

• ARIMA
• SARIMA
• RNN
• LSTM
• BPN

Abstract

Environment, price, regulation, and other factors influence the price of agricultural products, which is a social signal of product supply and demand. The price of many agricultural products fluctuates greatly due to the asymmetry between production and marketing details. Horticultural goods are particularly price sensitive because they cannot be stored for long periods of time. It is very important and helpful to forecast the price of horticultural products which is crucial in designing a cropping plan. The proposed method guides the farmers in agricultural product production and harvesting plans. Farmers can benefit from long-term forecasting since it helps them plan their planting and harvesting schedules. Customers can also profit from daily average price estimates for the short term. This paper study the time series models such as ARIMA, SARIMA, and neural network models such as BPN, LSTM and are used for wheat cost prediction in India. A large scale available data set is collected and tested. The results shows that since ARIMA and SARIMA models are well suited for small-scale, continuous, and periodic data, the BPN and LSTM provide more accurate and faster results for predicting well weekly and monthly trends of price fluctuation.

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