|Purpose: With the environmental changes in clothing distribution, the domestic clothing distribution path has changed dramatically. To respond to it, an in-depth study is required to conduct the clothing distribution paths. Therefore, this study attempts to draw suggestions on overall clothing distribution paths by employing the time-series predictive analysis. Research design, data, and methodology: The main purpose of this study was to analyze and predict the relations of distribution paths by categorizing four clothing distribution paths, such as a department store, a large mart, a retail shop, and a non-store retailing. In order to examine the predictive analysis, the ARIMA model was used. In addition, the VAR model of Granger Causality Test was employed to investigate the causal relations regarding distribution paths. Results: After confirming the data stabilty by the ADF, each distribution path model was established by the Autocorrelation Coefficient and the Partial Autocorrelation Coefficient. The study results predicted the ARIMA model such as a department store ARIMA(1,0,0)(0,1,0)12, a large mart ARIMA(0,1,1)(0,1,0)12, a retail shop ARIMA(0,1,2)(0,1,0)12, and a non-store retailing ARIMA(1,1,0) (1,1,0)12, respectively. Conclusions: In conclusion, according to the predictive analysis, the overall sale index of four clothing distribution paths were influenced by the seasons. More specific, this study results indicated that a large mart and a non-store retailing increased the overall sale index; however, a retail shop showed a decreased sale index. Additionally, the total sale index of the department store was consistent, but fluctuated by the seasons. Additionally, the study results provided that most distribution paths were influenced by the seasons and impacted the total sale index each other, except a department store and a retail shop.