Containerized Freight Index Prediction Integrating Deep Learning and Time Series Analysis

Abstract

In the context of globalization, the shipping industry, as a core pillar of international trade, is crucial for global economic stability. This study focuses on the China Containerized Freight Index (CCFI) and proposes a composite deep learning model combining CEEMDAN, CNN, LSTM, and SENet for CCFI analysis and prediction. Initially, the CEEMDAN algorithm is used to decompose the CCFI time series, extracting useful signals from noise. Then, CNN extracts features, LSTM model sequences, and SENet enhances feature representation. The CEEMDAN-CNN-LSTM-SENet model achieves superior predictive performance with MAE of 29.5171, RMSE of 33.7638, and MAPE of 1.56%. Compared to other models like ECL (MAE 77.0951, RMSE 57.3559, MAPE 3.25%), our model shows approximately 61.7% improvement in MAE. This research offers a robust tool for shipping market analysis and policy-making, providing new perspectives for handling complex economic data.