Agricultural product price prediction method based on EMD-ELM

Abstract

The invention discloses an agricultural product price prediction method based on EMD-ELM, and the method builds an agricultural product price combination prediction model based on empirical mode decomposition (EMD) and an extreme learning machine (ELM) method, and comprises the following steps: firstly collecting agricultural product price time series data; decomposing the original agricultural product price time sequence into a plurality of intrinsic mode functions (IMF) and remainders by utilizing empirical mode decomposition, then respectively predicting the components by using an extreme learning machine, and finally combining prediction results of the components to obtain a prediction value of the original agricultural product price time sequence. According to the invention, prediction is carried out in practical application and the prediction result is evaluated and analyzed, so that the prediction error is extremely small; and compared with prediction methods such as a BP neuralnetwork, the prediction method combining empirical mode decomposition and an extreme learning machine has good agricultural product price prediction performance and can be suitable for prediction ofagricultural product price fluctuation rules.

Description

technical field [0001] The invention belongs to the technical field of agricultural product data processing, and in particular relates to an EMD-ELM-based agricultural product price prediction method. Background technique [0002] The forecasting of agricultural product prices belongs to the category of time series forecasting. At the same time, agricultural products are highly corrosive and must meet the balance of supply and demand. This makes the forecasting of agricultural product prices different from that of general commodities. In reality, climate change, economic fluctuations, special holidays and many other external factors will have an impact on the price of agricultural products, which makes the price of agricultural products show a high degree of random volatility. This makes high-accuracy agricultural commodity price forecasts quite challenging. Through the analysis and forecasting of agricultural product prices, farmers and producers and operators are provided...

AI Technical Summary

Problems solved by technology

However, the price sequence of agricultural products is a special sequence signal with nonlinearity and non-stationarity. Although the extreme learning machine can fit the nonlinear part of the price sequence very well, the non-stationary part of the price of agricultural products will have a greater impact on the prediction effect. influences

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