Deep Learning Based Models: Basic LSTM, Bi LSTM, Stacked LSTM, CNN LSTM and Conv LSTM to Forecast Agricultural Commodities Prices

Ramasamy Murugesan · Eva Mishra · Akash Hari Krishnan

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Stock Market Forecasting Methods Smart Agriculture and AI Forecasting Techniques and Applications

Abstract

Abstract The literature argues that an accurate price prediction of agricultural goods is a quintessence to assure a good functioning of the economy all over the world. Research reveals that studies with application of deep learning in the tasks of agricultural price forecast on short historical agricultural prices data are very scarce and insist on the use of different methods of deep learning to predict and to this reaction of filling the gap, this study employs five versions of LSTM deep learning techniques for the task of five agricultural commodities prices prediction on univariate time series dataset of Rice, Wheat, Gram, Banana, and Groundnut spanning January 2000 to July 2020. The study obtained good forecasting results for all five commodities employing all the five LSTM models. The study validated the results with lower values of error metrics, MAE, MAPE, MSE, and RMSE and two paired t-test with hypothesis and confidence level of 95% as a measure of robustness. The study predicted the one month ahead future price for all the five commodities and compared it with actual prices using said LSTM models and obtained promising results.

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References

2021Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN)2018Time series forecasting of petroleum production using deep LSTM recurrent networks 2020A CNN–LSTM model for gold price time-series forecasting 2019Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series 2020Gold volatility prediction using a CNN-LSTM approach 2020Bayesian BILSTM approach for tourism demand forecasting 2020Machine learning model for Bitcoin exchange rate prediction using economic and technology determinants 2020Predictive analytics in Agriculture: Forecasting prices of Arecanuts in Kerala

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