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...
Main Author: | Ziaul Haque Munim |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2022-01-01
|
Series: | Maritime Transport Research |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2666822X22000089 |
Similar Items
-
Electricity Price Forecasting in the Danish Day-Ahead Market Using the TBATS, ANN and ARIMA Methods
by: Orhan Altuğ Karabiber, et al.
Published: (2019-03-01) -
Localizing weather forecasts for enhanced heat load forecast accuracy in urban district heating systems
by: Hjörleifur G. Bergsteinsson, et al.
Published: (2024-12-01) -
Freight rate and demand forecasting in road freight transportation using econometric and artificial intelligence methods
by: Edvardas Liachovičius, et al.
Published: (2023-12-01) -
Forecasting Indian Goods and Services Tax revenue using TBATS, ETS, Neural Networks, and hybrid time series models
by: P.V. Thayyib, et al.
Published: (2023-10-01) -
Estimating the term structure of mortality: an application to actuarial studies
by: Marzieh Vahdani, et al.
Published: (2021-12-01)