State-space TBATS model for container freight rate forecasting with improved accuracy

State-space TBATS model for container freight rate forecasting with improved accuracy

This study forecasts container freight rates using three seasonal univariate models — Seasonal Autoregressive Integrated Moving Average (SARIMA), Seasonal Neural Network Autoregression (SNNAR) and the state-space TBATS model. As a proxy for weekly container freight, China Container Freight Index (CC...

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Bibliographic Details
Main Author: Ziaul Haque Munim
Format: Article
Language:English
Published: Elsevier 2022-01-01
Series:Maritime Transport Research
Subjects:
Neural networks
Shipping industry
Recursive forecast
Machine learning
State-space model
Online Access:http://www.sciencedirect.com/science/article/pii/S2666822X22000089

Internet

http://www.sciencedirect.com/science/article/pii/S2666822X22000089

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