基于机器学习的农产品期货价格预测模型研究

Research on Agricultural Product Futures Price Prediction Model Based on Machine Learning

摘要: 面向农产品期货价格波动特征，基于机器学习构建预测模型，实现趋势识别与波动响应的高精度拟合。研究内容涵盖价格数据预编码、时序因子滑窗编码、LSTM深度网络配置与偏差残差修正等技术环节，形成完整预测流程。实验以真实期货交易数据为样本，在多时间步区间内检验模型预测效果。结果表明，真实价格与预测价格在趋势与波动阶段高度一致，残差分布集中稳定，误差幅度较低，验证方法在捕捉价格动态特征方面具备较强适应性与时序稳定性。

Abstract: Focusing on the fluctuation characteristics of agricultural product futures prices, a prediction model is constructed based on machine learning to achieve high-precision fitting of trend identification and volatility response. The research content covers technical links such as price data pre-coding, time series factor sliding window encoding, LSTM deep network configuration and deviation residual correction, forming a complete prediction process. Experiments use real futures trading data as samples and test the model’s prediction effect within multiple time steps. The results show that the real price and the predicted price are highly consistent in terms of trend and volatility stages, the residual distribution is concentrated and stable, and the error range is low. This verification method has strong adaptability and temporal stability in capturing price dynamic characteristics.