School of Economics and Management, Tongji University, Shanghai 200092, China.
School of Public Administration, Hainan University, Haikou 570228, China.
Comput Intell Neurosci. 2021 Oct 31;2021:4471044. doi: 10.1155/2021/4471044. eCollection 2021.
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.
从宏观角度来看，农产品期货指数可以反映宏观经济走势，对可能出现的危机具有预警作用，为政府经济预测和宏观调控提供参考。因此，有必要加强对农产品期货价格预警预测的研究。对于期货价格的预测，常见的模型有两种：一种是传统的经典时间序列模型，另一种是人工智能浪潮下的神经网络模型。本文选取了 2012 年 1 月 10 日至 2020 年 2 月 27 日期间的农产品期货指数 1976 个收盘价作为研究数据，分别采用时间序列差分自回归求和移动平均模型（ARIMA 模型）和长短时记忆模型（LSTM 模型）对其进行研究，并在一些指标上对比了两种模型的预测效果。基于两种模型的预测结果，建立了一个简单的交易策略，并对两种模型的交易效果进行了比较。结果表明，LSTM 模型在农产品期货市场价格指数预测方面明显优于 ARIMA 时间序列模型。
