Forecasting Shanghai Containerized Freight Index by Using Time Series Models

Abstract

Recently, the container shipping industry has become unpredictable due to volatility and major events affecting the maritime sector. At the same time, approaches to estimating container freight rates using econometric and time series modelling have become very important. Therefore, in this paper, different time-series models have been explored that are related to the Shanghai Containerized Freight Index (SCFI). SMA, EWMA, and, SES, Holt Winter method are used to describe the data and model. Afterward, the Holt Winter method and SARIMA was applied to model and predict the SCFI index. MAPE, RMSE, AIC, BIC are used to measure the performances of the models and predictions. We observe that the SARIMA model provides comparatively better results than the existing freight rate forecasting models while performing short-term forecasts on a monthly rate. Results demonstrate that the increase will continue without losing momentum.