Department of Statistics and Econometrics, University of Erlangen-Nürnberg, Lange Gasse 20, 90403 Nuremberg, Germany.
Sensors (Basel). 2022 Sep 12;22(18):6884. doi: 10.3390/s22186884.
This paper develops a multi-dimensional Dynamic Time Warping (DTW) algorithm to identify varying lead-lag relationships between two different time series. Specifically, this manuscript contributes to the literature by improving upon the use towards lead-lag estimation. Our two-step procedure computes the multi-dimensional DTW alignment with the aid of shapeDTW and then utilises the output to extract the estimated time-varying lead-lag relationship between the original time series. Next, our extensive simulation study analyses the performance of the algorithm compared to the state-of-the-art methods Thermal Optimal Path (TOP), Symmetric Thermal Optimal Path (TOPS), Rolling Cross-Correlation (RCC), Dynamic Time Warping (DTW), and Derivative Dynamic Time Warping (DDTW). We observe a strong outperformance of the algorithm regarding efficiency, robustness, and feasibility.
本文提出了一种多维动态时间规整（DTW）算法，用于识别两个不同时间序列之间的时变超前-滞后关系。具体来说，本文通过改进用于超前-滞后估计的方法，为文献做出了贡献。我们的两步程序借助 shapeDTW 计算多维 DTW 对齐，然后利用输出结果从原始时间序列中提取估计的时变超前-滞后关系。接下来，我们广泛的模拟研究分析了与最先进的方法（热最优路径（TOP）、对称热最优路径（TOPS）、滚动互相关（RCC）、动态时间规整（DTW）和导数动态时间规整（DDTW））相比，该算法的性能。我们观察到该算法在效率、稳健性和可行性方面具有很强的优势。
