Time Series Analysis and Algorithm Application of Agricultural Product Price Forecast

The purpose of this study is to improve the ability to accurately predict the future trend of agricultural product prices through time series analysis and the application of advanced algorithms. Through the comprehensive analysis of the historical data of agricultural product prices, this study reveals the hidden trend, seasonality and periodicity factors, and uses nonlinear autoregressive neural networks (NARNN) to forecast the price. Time series analysis has been proven to be an effective tool to reveal the dynamic changes behind the prices of agricultural products. Through the mining of historical price data, we can understand the root cause of market fluctuation more comprehensively and provide the basis for future agricultural decision-making. In the aspect of algorithm application, we adopt NARNN. The results show that NARNN has potential to improve the accuracy of agricultural product price prediction. In particular, it performs well in dealing with nonlinear relationships and large-scale data sets. By accurately forecasting the future price trend, the government can more effectively formulate policies to regulate the market and support agricultural producers, so as to promote the sustainable development of agriculture. It is considered that agricultural producers, governments and market participants provide more reliable decision support.