Family 模型的大蒜价格预测方法,Applied Sciences

基于组合 LSTM 和 GARCH-Family 模型的大蒜价格预测方法
Applied Sciences ( IF 2.838 ) Pub Date : 2022-11-09 , DOI: 10.3390/app122211366
Yan Wang , Pingzeng Liu , Ke Zhu , Lining Liu , Yan Zhang , Guangli Xu

大蒜价格频繁剧烈波动，严重影响大蒜产业的可持续发展。准确预测大蒜价格，有助于大蒜从业者正确评估和科学决策，从而规避市场风险，促进大蒜产业健康发展。为提高大蒜价格的预测精度，针对非平稳和非线性特征，提出了一种基于长短期记忆（LSTM）和多个广义自回归条件异方差（GARCH）族模型相结合的大蒜价格预测方法。大蒜价格系列。首先，通过构建GARCH族模型，得到大蒜价格序列的波动率聚合等波动特征信息。然后，我们利用 LSTM 模型来学习大蒜价格系列与该系列波动特征信息之间复杂的非线性关系，并预测大蒜价格。我们将提出的模型应用于现实世界的大蒜数据集。实验结果表明，包含大蒜价格波动特征信息的 LSTM 和 GARCH-family 组合模型的预测性能普遍优于单独模型的预测性能。结合 GARCH 和 PGARCH 模型的 LSTM 组合模型 (LSTM-GP) 在平均绝对误差、均方根误差和平均绝对百分比误差等评价指标方面对大蒜价格的预测性能最好。

A Garlic-Price-Prediction Approach Based on Combined LSTM and GARCH-Family Model

The frequent and sharp fluctuations in garlic prices seriously affect the sustainable development of the garlic industry. Accurate prediction of garlic prices can facilitate correct evaluation and scientific decision making by garlic practitioners, thereby avoiding market risks and promoting the healthy development of the garlic industry. To improve the prediction accuracy of garlic prices, this paper proposes a garlic-price-prediction method based on a combination of long short-term memory (LSTM) and multiple generalized autoregressive conditional heteroskedasticity (GARCH)-family models for the nonstationary and nonlinear characteristics of garlic-price series. Firstly, we obtain volatility characteristic information such as the volatility aggregation of garlic-price series by constructing GARCH-family models. Then, we leverage the LSTM model to learn the complex nonlinear relationships between the garlic-price series and the volatility characteristic information of the series, and predict the garlic price. We applied the proposed model to a real-world garlic dataset. The experimental results show that the prediction performance of the combined LSTM and GARCH-family model containing volatility characteristic information of garlic price is generally better than those of the separate models. The combined LSTM model incorporating GARCH and PGARCH models (LSTM-GP) had the best performance in predicting garlic price in terms of evaluation indexes, such as mean absolute error, root mean-square error, and mean absolute percentage error. The combined model of LSTM-GARCH provides the best results in garlic price prediction and can provide support for garlic price prediction.