Bootstrap method was initially used to determine accuracy measures for sample estimates of independent and identical distributions (i.i.d.). In order to apply bootstrap method to time-dependent data, blocking technique is introduced to preserve serial correlation of the original time series data. In the past, resampling techniques for time-dependent data were implemented using Non-overlapping Block Bootstrap (NBB) method but its dichotomous block arrangement restricts the number of blocks. As a result, improvement becomes necessary. Although the Moving Block Bootstrap (MBB) method improves upon NBB with regard to many more blocks, it introduces an uneven representation of the time series elements which eventually influences its accuracy. In this paper, an innovative method called Moving Block Bootstrap Method with better element Representation (MBBR) is developed to ensure that the time series elements within the block are better represented with minimum number of elements. To compare MBB and MBBR, simulated studies were carried out on some set time series data following each classes of Autoregressive Moving Average (ARMA) model with different parameters, sample sizes and standard deviation using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Results show that by improving the representation of time series data in the blocking arrangement, the accuracy of the proposed method (MBBR) consistently outperforms the existing one (MBB) and thus, provides more efficient estimates of the dependent variable. [ABSTRACT FROM AUTHOR]