论文标题
使用贝叶斯网络从多个深度学习者中选择数据自适应学习者
Selecting Data Adaptive Learner from Multiple Deep Learners using Bayesian Networks
论文作者
论文摘要
提出了一种使用多个深度学习者和贝叶斯网络预测时间序列的方法。在这项研究中，输入解释变量是与学习者相关的贝叶斯网络节点。使用K-均值聚类对培训数据进行划分，并根据集群对多个深度学习者进行培训。贝叶斯网络用于确定哪个深层学习者负责预测时间序列。我们确定一个阈值值，并选择具有等于或大于阈值值的后验概率的选择者，这可能有助于更健壮的预测。提出的方法应用于财务时间序列数据，并证明了Nikkei 225指数的预测结果。
A method to predict time-series using multiple deep learners and a Bayesian network is proposed. In this study, the input explanatory variables are Bayesian network nodes that are associated with learners. Training data are divided using K-means clustering, and multiple deep learners are trained depending on the cluster. A Bayesian network is used to determine which deep learner is in charge of predicting a time-series. We determine a threshold value and select learners with a posterior probability equal to or greater than the threshold value, which could facilitate more robust prediction. The proposed method is applied to financial time-series data, and the predicted results for the Nikkei 225 index are demonstrated.
