Short-Term Price Forecasting For Agro-products Using Artificial Neural Networks

Gan-qiong Li · Zhe-min Li

51 citations0/0 files storedAgriculture and Agricultural Science Procedia

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Stock Market Forecasting Methods Economics of Agriculture and Food Markets Forecasting Techniques and Applications

Abstract

It is well known that short-term market price forecasting has been a difficult problem for a long time because of too many factors which can not be accurately predicted. Conventionally, time series analysis has been often employed in modeling short-term price forecasts. In recent years a new technique of artificial neural networks (ANN) has been proposed as an efficient tool for modeling and forecasting. A feed-forward ANN model has been developed for short-term price forecasting of tomato and in comparison with time series model ARIMA in this study. The data used include daily wholesale price, weekly wholesale price and monthly wholesale price collected between 1996 and 2010. The results showed that ANN model evidently outperformed the time series model in forecasting the price before one day or one week. A good correlation between the modeled and the real prices was observed from the feed-forward ANN model, with a relative error less than 5.0%.

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References

2005The use of data mining and neural networks for forecasting stock market returns 2009Forecasting stock market short-term trends using a neuro-fuzzy based methodology 2008Improving artificial neural networks’ performance in seasonal time series forecasting 2005Neural network approach to forecasting of quasiperiodic financial time series 2004Evolving Time Series Forecasting ARMA Models 2006Forecasting Economic Data with Neural Networks

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