论文标题
自动深度学习时间序列数据的趋势预测
Automatic deep learning for trend prediction in time series data
论文作者
论文摘要
最近，已经探索了深层神经网络（DNN）算法，以预测时间序列数据的趋势。在许多现实世界应用中，时间序列数据是从动态系统捕获的。 DNN模型在更新并进行重新培训时必须提供稳定的性能。在这项工作中，我们探讨了自动机器学习技术的使用来自动化算法选择和趋势预测的超参数优化过程。我们演示了最近的Automl工具，特别是HPBandster框架如何有效地用于自动化DNN模型开发。我们的自动实验发现了最佳配置，这些配置产生了模型，这些模型与在四个数据集的手动实验过程中发现的平均性能和稳定性水平进行了比较。
Recently, Deep Neural Network (DNN) algorithms have been explored for predicting trends in time series data. In many real world applications, time series data are captured from dynamic systems. DNN models must provide stable performance when they are updated and retrained as new observations becomes available. In this work we explore the use of automatic machine learning techniques to automate the algorithm selection and hyperparameter optimisation process for trend prediction. We demonstrate how a recent AutoML tool, specifically the HpBandSter framework, can be effectively used to automate DNN model development. Our AutoML experiments found optimal configurations that produced models that compared well against the average performance and stability levels of configurations found during the manual experiments across four data sets.
