<div><strong>Background:</strong> Agricultural price forecasting is one of the research hotspots in time series forecasting due to its unique characteristics. In this paper, we develop a standard long short-term memory (LSTM) for accurately predicting a nonstationary and nonlinear agricultural price series.&nbsp;<br />
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<strong>Methods:</strong> An LSTM model effectively analyses and captures short-term and long-term temporal patterns of a complex time series due to its recurrent neural architecture and the memory function used in the hidden nodes.&nbsp;<br />
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<strong>Result:</strong> The empirical results using the international monthly price series of maize demonstrate the superiority of the developed LSTM model over other models in terms of various forecasting evaluation criteria. Overall, LSTM model shows great potential for improving the accuracy and reliability of agricultural price predictions, benefiting farmers, traders, and policymakers in making informed decisions.</div>


<p>Agricultural Commodity Price Prediction using Long Short-Term Memory (LSTM) based Neural Networks</p>


<div style="text-align:justify">Timely and accurate price forecasting is one of challenges in agriculture. It helps both producer and consumer to make the efficient plan. The inherent nonstationarity and nonlinearity in price data makes problem in forecasting. A single forecasting model may not be able to tackle nonstationarity and nonlinearity, simultaneously. With this context, a nonlinear hybrid model called EMD-SVR has been proposed to deal the problem. The empirical mode decomposition (EMD) deals with nonstationarity by decomposing price data into a finite and small number of subsets. Further, these decomposed subsets are forecasted using Support Vector Regression (SVR) model and aggregated to make final forecast. The performance of the proposed hybrid model are evaluated in monthly price index of chili. The empirical results indicated the superiority of the EMD-SVR model.</div>


Agricultural Commodity Price Prediction using Long Short-Term Memory (LSTM) based Neural Networks

“EMD-SVR” Hybrid Machine Learning Model and its Application in Agricultural Price Forecasting