Autocorrelated statistical process control that is widely employed in process control environments typically uses the autoregressive integrated moving average (ARIMA) model in fitting autocorrelated time series data. Nevertheless, the iterative modeling procedures of ARIMA are laborious, time-consuming, expensive, and complex. Meanwhile, autocorrelated data is governed by the geometric Brownian motion (GBM) law if its logarithmic returns are independent and identically normally distributed (i.i.n.d.). By utilizing these attributes, this paper aims to propose the Logarithmic Return (LR) model as an alternative methodology in modeling time series data. Twelve real-world datasets are used to demonstrate the applicability of the proposed model. All computations are implemented via R-programming language. In addition to being parsimonious and easy to compute, the LR model is reported with a shorter Central Processing Unit (CPU) running time. Specifically, it typically takes an average of less than 0.20 seconds to obtain the LR model using twelve datasets, while its counterpart requires over 5 seconds. LR model has a comparable good mean average percentage error (MAPE) to the ARIMA model, thus LR model is as accurate as the ARIMA model. This study shows that the LR model with two parameters and requires a two-step implementation procedure is a promising alternative model of ARIMA for positive datasets in time series modeling.