Abstract
From a macro perspective, futures index of agricultural products can reflect the trend of macroeconomy and can also have an early warning effect on the possible crisis and provide a reference for the government's economic forecast and macro control. Therefore, it is necessary to strengthen the research on early warning and prediction of agricultural futures price. For the prediction of futures price, there are two kinds of common models: one is the traditional classic time series model, and the other is the neural network model under the wave of artificial intelligence. This paper selects the 1976 closing data of agricultural futures index from January 10, 2012, to February 27, 2020, and uses the time series differential autoregressive integrated moving average model (ARIMA model) and long short-term memory model (LSTM model) to study this work, respectively, and compares the predicted effects of the two models in some metrics. Based on the predicted results of the two models, a simple trading strategy is established, and the trading effects of the two models are compared. The results show that the LSTM model has obvious advantage over ARIMA time series model in the price index prediction of agricultural futures market.
Highlights
In developed countries, commodity futures index has been running for decades and has become an important weathervane to reflect the trend of the financial market
As a part of commodity futures, agricultural futures get less attention, so the research on the price prediction of agricultural futures will provide some reference for the existing academic research [2]. e price rise of agricultural products will increase consumer spending, which may lead to inflation
Food affects the consumer price index (CPI) rise by 3.82%, which has become the main force affecting the CPI rise. e most important raw materials for the food industry are agricultural products. erefore, the impact of changes in the price of agricultural products on CPI is huge [3]. e research shows that the futures price index of agricultural products can reflect the real-time trend of CPI 3–6 months in advance. erefore, from the macro level, the futures price index of agricultural products can reflect the macroeconomic trend, can have an early warning effect on the possible crisis, and can provide reference for the government’s economic forecast and macro control [4]
Summary
Commodity futures index has been running for decades and has become an important weathervane to reflect the trend of the financial market. E research shows that the futures price index of agricultural products can reflect the real-time trend of CPI 3–6 months in advance. Erefore, from the macro level, the futures price index of agricultural products can reflect the macroeconomic trend, can have an early warning effect on the possible crisis, and can provide reference for the government’s economic forecast and macro control [4]. For investors, the agricultural futures price index can reflect the overall trend of the agricultural futures market, help investors grasp the market trend in a more timely manner, Computational Intelligence and Neuroscience adjust the proportion of investment, and provide reference for investors to carry out arbitrage trading of specific varieties, so as to maximize profits [6]. Is paper is divided into five parts: the first part is the background of this work; the second part is the related work; it mainly analyses some current researches of ARIMA model and LSTM model and their application in agricultural futures market; the third part is the construction process for the improved LSTM model; it mainly introduces the model based on RNN model; the fourth part is the comparative experiment, comparing the forecasting effect of the LSTM and ARIMA models on the price index series of Zhengzhou commodity futures and Dalian commodity. e fifth part concludes this work and presents some findings
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