"No Free Lunch Theorem"and Algorithm Selection in Policy Research:Predicting Hog Price with Machine Learning
Agricultural product pricing policy is a key element of agricultural policy,where accurate prediction of price fluctuations is the crucial foundation for policy formulation.Breakthroughs in Artificial Intelligence(AI)have provided new and powerful tools for agricultural price analysis and prediction,and how to choose appropriate model algorithms to analyze and predict prices has become a research topic.This study employs and compares four machine learning models:the traditional Autore-gressive Integrated Moving Average(ARIMA)model,Convolution Neural Network(CNN),Recurrent Neural Network(RNN),and Long Short-Term Memory(LSTM)model,for analyzing and forecasting the changes in China's pork prices.We find that the performance of the traditional ARIMA model does not show a significant difference from that of the LSTM model,but both significantly outperform the CNN and RNN models.Considering extreme price variations,the LSTM model may slightly outperform the ARIMA model.This result supports,to some extent,the"No Free Lunch Theorem"in machine learning,which suggests that no single algorithm is superior for all types of research problems.Therefore,it is imperative to employ a variety of algorithms in empirical research to find the most effec-tive one.Furthermore,the"Population Stability Index(PSI)"indicates significant structure change in China's pork prices around 2018,necessitating continual updates to the models in response to changes in data structures.
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