Time series data is an important object of data mining. In analysis of time series, misjudgment of correlation will occur if time lags are not considered. Therefore, there exists mutual restraint between correlation and time lags in time series. Based on the exploration of correlation and simultaneousness of time series, the correlation identification and curve registration methods for double sequences are given in this paper. Concretely, the study investigates the reasons and characteristics of two types of errors in correlation analysis in the view of time warping, and then deduces the correlation coefficient’s bounds in a certain significance level by its asymptotic distribution. Further, a correlation identification method based on time-lag series is proposed. Finally, the curve registration model of maximizing the correlation coefficient is presented with a broader application than AISE. Smoothing-generalized expectation maximization (S-GEM) algorithm is used to solve the time warping function of the new model. The experimental results on simulated and real data demonstrate that the proposed correlation identification approach is more effective than 3 correlation coefficients and Granger causality test in recognition of spurious regression. The registration method provided is obviously performed better than the classical continuous monotone registration method (CMRM), Self-modeling registration (SMR) and maximum likelihood registration (MLR) in most situations. Linear correlation of double series and functional curve registration are considered here, and the results can provide the theoretical basis for correlation identification and time alignment in regression and reference direction for correlation analysis and curves registration of multiple series.