论文标题
案例研究时间序列回归中动态时间扭曲的影响
A Case-Study on the Impact of Dynamic Time Warping in Time Series Regression
论文作者
论文摘要
众所周知，动态的时间扭曲（DTW）可以有效揭示不完美对齐的时间序列之间的相似之处。在本文中，我们在光谱时间序列数据上说明了这一点。我们表明，当仅考虑单个波长时，DTW可以有效提高回归任务的准确性。当与k-nearest邻居结合使用时，DTW具有额外的优势，即可以在时间序列的水平上揭示样品之间的相似性和差异。但是，在这个问题中，我们在这里考虑数据可在一系列波长范围内获得。如果在许多波长中使用了骨料统计数据（均值，方差），则DTW的好处不再显而易见。我们将其表示为一个情况，即大数据在机器学习中胜过复杂的模型。
It is well understood that Dynamic Time Warping (DTW) is effective in revealing similarities between time series that do not align perfectly. In this paper, we illustrate this on spectroscopy time-series data. We show that DTW is effective in improving accuracy on a regression task when only a single wavelength is considered. When combined with k-Nearest Neighbour, DTW has the added advantage that it can reveal similarities and differences between samples at the level of the time-series. However, in the problem, we consider here data is available across a spectrum of wavelengths. If aggregate statistics (means, variances) are used across many wavelengths the benefits of DTW are no longer apparent. We present this as another example of a situation where big data trumps sophisticated models in Machine Learning.
