The shipping freight rate index is an indicator of fluctuations in the cost of shipping raw materials and commodities. It is used to diagnose and predict changes in the global real economy and in the shipping market. Thus, both financial institutions and the shipping industry need to strengthen their capacity to forecast and analyze the index. In this study, we conduct a shortterm forecast of the Baltic Dry Index, a representative maritime freight rate index, using the time-series factor decomposition method. To verify our model’s predictive power, we apply the prediction method presented in this study to past data with different sample intervals and perform a backtest to compare the predicted values with actual observations. The root mean square forecast error of the backtest shows that our prediction model outperforms the random walk model. The results suggest that it is possible to conduct a significant short-term forecast of the shipping freight rate index because it involves a short-term trend and seasonality. These time-series characteristics of the shipping freight rate can be used to quantitatively provide short-term market forecasts.