论文标题
时间序列的聚类方法使用神经网络预测：基于距离与基于特征的聚类方法的比较研究
A clustering approach to time series forecasting using neural networks: A comparative study on distance-based vs. feature-based clustering methods
论文作者
论文摘要
时间序列预测最近引起了很多关注。这是因为许多现实现象可以作为时间序列建模。计算机的处理能力的大量数据和最新进步使研究人员能够开发更复杂的机器学习算法，例如神经网络，以预测时间序列数据。在本文中，我们提出了各种神经网络体系结构，以使用动态测量结果来预测时间序列数据。此外，我们介绍了有关如何结合静态和动态测量的各种架构。我们还研究了执行技术（例如异常检测和聚类对预测精度）的重要性。我们的结果表明，聚类可以改善总体预测时间，并改善神经网络的预测性能。此外，我们表明，基于功能的聚类可以在速度和效率方面胜过基于距离的聚类。最后，我们的结果表明，添加更多预测因子预测目标变量不一定会提高预测准确性。
Time series forecasting has gained lots of attention recently; this is because many real-world phenomena can be modeled as time series. The massive volume of data and recent advancements in the processing power of the computers enable researchers to develop more sophisticated machine learning algorithms such as neural networks to forecast the time series data. In this paper, we propose various neural network architectures to forecast the time series data using the dynamic measurements; moreover, we introduce various architectures on how to combine static and dynamic measurements for forecasting. We also investigate the importance of performing techniques such as anomaly detection and clustering on forecasting accuracy. Our results indicate that clustering can improve the overall prediction time as well as improve the forecasting performance of the neural network. Furthermore, we show that feature-based clustering can outperform the distance-based clustering in terms of speed and efficiency. Finally, our results indicate that adding more predictors to forecast the target variable will not necessarily improve the forecasting accuracy.
