Research on Correlation Analysis Method of Time Series Features Based on Dynamic Time Warping Algorithm

Rich datasets related to the Earth have been obtained because of the rapid development of Earth observation technologies. A broad range of prior research has investigated how to obtain the correlation relationships of relevant features from a large number of data containing spatio-temporal information, which is also the technical basis for big data analysis. Based on the dynamic time warping (DTW) algorithm, this research proposes a correlation analysis method of time series data and applies it to the correlation analysis between the time series features of the surface temperature and melting area of the Antarctic ice sheet. The results show that our method based on the DTW algorithm can effectively distinguish the change details of the nonlinear time series. The correlation coefficients between these two highly correlated factors computed by our method are higher than Pearson's correlation coefficients by more than 0.2 almost in all study areas where data are available. In summary, the method proposed in this study provides a new feasible way for the correlation study of time series data. It outperforms than traditional correlation coefficients such as Pearson's correlation coefficient in some fields, specifically when complex nonlinear time series data with certain periodicity are used.

Automated summary

Rich datasets related to the Earth have been obtained due to the rapid development of Earth observation technologies. This research proposes a correlation analysis method of time series data based on the dynamic time warping (DTW) algorithm, and applies it to the correlation analysis between the time series features of the surface temperature and melting area of the Antarctic ice sheet. The results show that the proposed method can effectively distinguish the change details of nonlinear time series and outperforms traditional correlation coefficients like Pearson's correlation coefficient.