论文标题
对预测时间序列数据趋势的深神经网络的分析
An analysis of deep neural networks for predicting trends in time series data
论文作者
论文摘要
最近，提出了一种混合深度神经网络（DNN）算法，用于预测时间序列数据的趋势。虽然Trenet被证明对趋势预测的性能优于其他DNN和传统ML方法，但所使用的验证方法没有考虑到时间序列数据集的顺序性质，也没有处理模型更新。在这项研究中，我们使用步行向前验证方法在相同的数据集上复制了Trenet实验，并在多个独立运行中测试了我们的最佳模型以评估模型稳定性。我们将四个数据集的混合型Trenet算法的性能与获取点数据的Vanilla DNN算法以及传统的ML算法进行了比较。我们发现，总的来说，Trenet的性能仍然比原始Trenet研究中报道的所有数据集都表现更好，但并未在所有数据集中。这项研究强调了使用适当的验证方法和评估模型稳定性评估和开发机器学习模型以在时间序列数据中预测趋势预测的重要性。
Recently, a hybrid Deep Neural Network (DNN) algorithm, TreNet was proposed for predicting trends in time series data. While TreNet was shown to have superior performance for trend prediction to other DNN and traditional ML approaches, the validation method used did not take into account the sequential nature of time series data sets and did not deal with model update. In this research we replicated the TreNet experiments on the same data sets using a walk-forward validation method and tested our optimal model over multiple independent runs to evaluate model stability. We compared the performance of the hybrid TreNet algorithm, on four data sets to vanilla DNN algorithms that take in point data, and also to traditional ML algorithms. We found that in general TreNet still performs better than the vanilla DNN models, but not on all data sets as reported in the original TreNet study. This study highlights the importance of using an appropriate validation method and evaluating model stability for evaluating and developing machine learning models for trend prediction in time series data.
