WO2021082811A1 - Procédé de prédiction de séries chronologiques d'opérations de change - Google Patents

Procédé de prédiction de séries chronologiques d'opérations de change Download PDF

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WO2021082811A1
WO2021082811A1 PCT/CN2020/116955 CN2020116955W WO2021082811A1 WO 2021082811 A1 WO2021082811 A1 WO 2021082811A1 CN 2020116955 W CN2020116955 W CN 2020116955W WO 2021082811 A1 WO2021082811 A1 WO 2021082811A1
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neural network
foreign exchange
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倪丽娜
李玉洁
张金泉
张泽坤
亓亮
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山东科技大学
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  • Dash Rajashree proposed an evolutionary framework that uses an improved hybrid leapfrog algorithm and artificial neural network to predict foreign exchange time series data. And compared with the hybrid leapfrog algorithm and particle swarm optimization algorithm, the experiment shows that the model proposed in the article is more suitable for foreign exchange time series analysis.
  • the long and short-term memory network is performed separately, and the output of the two algorithms is combined to make the final prediction.
  • Figure 14 is a graph showing the change trend of training speed with the increase in the number of GPUs
  • Figure 19 is a fitting diagram of the prediction effect of the LSTM prediction method
  • RSV N (Close (N) -Low (N) ) ⁇ (High (N) -Low (N) ) ⁇ 100% (8)
  • the number of lag periods n refers to the length of the analysis and prediction time series, that is, the n+1 day is predicted using the data of the previous n days.
  • the difference in the number of lag periods may have an important impact on forecast accuracy.
  • choose 5, 10, 20, 30, 40, 50, 60 different lag periods study the influence of the lag period n on the prediction accuracy, and select the best lag period n.
  • the detailed laboratory data is shown in Table 3, and the data in Table 3 is visualized to get Figure 6.
  • the convolution kernel is 1 ⁇ 1
  • the convolution kernel is larger than 3 ⁇ 3
  • the spatial features around the data are collected too much .
  • the prediction accuracy shows that the location far away from the data has less correlation with the current data.
  • the number of convolutional layers is 2 and the convolution sum size is 3 ⁇ 3, the spatial characteristics of the data are better abstracted. Therefore, the number of convolutional layers is set to 2, and the size of the convolution kernel is set. It is 3 ⁇ 3.

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Abstract

Procédé de prédiction de séries chronologiques d'opérations de change, se rapportant au domaine des données de séries chronologiques d'opérations de change. Selon le procédé de prédiction, des données de séries chronologiques d'opérations de change sont analysées et prédites sur la base d'un algorithme d'apprentissage profond C-LSTM, qui combine un réseau neuronal convolutionnel avec un long réseau à mémoire à court-terme, et un procédé de prédiction à court terme pour une série chronologique d'opérations de change. Trois types de facteurs principaux affectant la précision de prédiction sont systématiquement étudiés. La caractéristique d'entrée optimale, la structure de réseau et le procédé de formation sont sélectionnés. En ce qui concerne le problème du bruit de mégadonnées, un algorithme d'optimisation de caractéristiques est construit sur la base d'un PCA pour effectuer une réduction de dimension et un débruitage sur des caractéristiques d'entrée, et des procédés de perte et régularisation L2 sont ensuite utilisés pour éviter le problème de surapprentissage, ce qui permet d'améliorer davantage la précision de prédiction du procédé de prédiction. En même temps, afin de répondre à l'exigence d'un marché d'opérations de change pour une efficacité élevée dans le temps, un algorithme d'optimisation parallèle est construit sur la base d'une technologie de calcul GPU haute-performance, ce qui permet d'augmenter la vitesse de formation d'un modèle de réseau et d'améliorer la disponibilité du procédé de prédiction dans des scénarios d'application réels.
PCT/CN2020/116955 2019-10-29 2020-09-23 Procédé de prédiction de séries chronologiques d'opérations de change WO2021082811A1 (fr)

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CN113569473A (zh) * 2021-07-19 2021-10-29 浙江大学 基于多项式特征lstm粒度计算的空分管网氧气长期预测方法
CN113642225A (zh) * 2021-05-24 2021-11-12 国网新疆电力有限公司经济技术研究院 一种基于attention机制的CNN-LSTM短期风电功率预测方法
CN113724780A (zh) * 2021-09-16 2021-11-30 上海交通大学 基于深度学习的蛋白质卷曲螺旋结构特征预测实现方法
CN113920408A (zh) * 2021-09-10 2022-01-11 天津理工大学 一种基于cnn-rnn并行融合的光子晶体透射光谱序列特征提取方法
CN114205138A (zh) * 2021-12-09 2022-03-18 麒麟软件有限公司 针对容器云平台的网络入侵检测方法
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CN115456073A (zh) * 2022-09-14 2022-12-09 杭州电子科技大学 基于长短期记忆的生成式对抗网络模型建模分析方法
WO2023169589A1 (fr) * 2022-03-07 2023-09-14 东南大学 Procédé de modélisation de canal prédictif basé sur un réseau antagoniste et un réseau de mémoire à court et long terme
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CN113642225A (zh) * 2021-05-24 2021-11-12 国网新疆电力有限公司经济技术研究院 一种基于attention机制的CNN-LSTM短期风电功率预测方法
CN113569473A (zh) * 2021-07-19 2021-10-29 浙江大学 基于多项式特征lstm粒度计算的空分管网氧气长期预测方法
CN113554873A (zh) * 2021-07-20 2021-10-26 重庆大学 一种基于高阶矩的行程时间波动性预测方法及装置
CN113920408A (zh) * 2021-09-10 2022-01-11 天津理工大学 一种基于cnn-rnn并行融合的光子晶体透射光谱序列特征提取方法
CN113724780A (zh) * 2021-09-16 2021-11-30 上海交通大学 基于深度学习的蛋白质卷曲螺旋结构特征预测实现方法
CN113724780B (zh) * 2021-09-16 2023-10-13 上海交通大学 基于深度学习的蛋白质卷曲螺旋结构特征预测实现方法
CN114205138A (zh) * 2021-12-09 2022-03-18 麒麟软件有限公司 针对容器云平台的网络入侵检测方法
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CN114387030B (zh) * 2022-01-13 2024-03-15 瑞祥全球购超市有限公司 一种面向网络购物平台的在线用户量的智能分析方法
WO2023169589A1 (fr) * 2022-03-07 2023-09-14 东南大学 Procédé de modélisation de canal prédictif basé sur un réseau antagoniste et un réseau de mémoire à court et long terme
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CN115018553A (zh) * 2022-06-30 2022-09-06 东南大学 基于深度学习的区域物流单量预测系统及方法
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CN115456073B (zh) * 2022-09-14 2023-07-07 杭州电子科技大学 基于长短期记忆的生成式对抗网络模型建模分析方法
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