This study considers testing for one-sample mean differences in high-dimensional temporally dependent data. To eliminate the bias caused by the temporal dependence in the time series observations, we propose a new statistic to estimate the squared Euclidean distance between the two means that excludes diagonal-products of data vectors of temporally close time points. We derive the asymptotic normality of the proposed statistic for the high-dimensional setting. A numerical simulation and a real-data analysis on the return and volatility of S&P 500 stocks before and after the 2008 financial crisis demonstrate the performance and utility of the proposed test.