A new approach for Baltic Dry Index forecasting based on empirical mode decomposition and neural networks

被引：41

作者：

Zeng, Qingcheng ^{[1
]}

Qu, Chenrui ^{[1
]}

Ng, Adolf K. Y. ^{[2
]}

Zhao, Xiaofeng ^{[1
]}

机构：

[1] Dalian Maritime Univ, Sch Transportat Management, 1 Linghai Rd, Dalian 116026, Peoples R China

[2] Univ Manitoba, Dept Supply Chain Management, IH Asper Sch Business, Winnipeg, MB R3T 5V4, Canada

来源：

MARITIME ECONOMICS & LOGISTICS | 2016年 / 18卷 / 02期

基金：

中国国家自然科学基金;

关键词：

dry bulk shipping market; empirical mode decomposition; artificial neural networks; forecasting; Baltic Dry Index (BDI); TIME-SERIES; RATES; PREDICTION; SPOT; EMD;

D O I：

10.1057/mel.2015.2

中图分类号：

U [交通运输];

学科分类号：

08 ; 0823 ;

摘要：

In this article, a method based on empirical mode decomposition (EMD) and artificial neural networks (ANN) is developed for Baltic Dry Index (BDI) forecasting. The original BDI series is decomposed into several independent intrinsic mode functions (IMFs) using EMD first. Then the IMFs are composed into three components: short-term fluctuations, effect of extreme events and long-term trend. On the basis of results of decomposition and composition, ANN is used to model each IMF and composed component. Results show that the proposed EMD-ANN method outperforms ANN and VAR. The EMD-based method thus provides a useful technique for dry bulk market analysis and forecasting.

引用

页码：192 / 210

页数：19

共 50 条

[1] A new approach for Baltic Dry Index forecasting based on empirical mode decomposition and neural networks
Qingcheng Zeng
Chenrui Qu
Adolf K.Y. Ng
Xiaofeng Zhao
Maritime Economics & Logistics, 2016, 18 : 192 - 210
[2] An approach for Baltic Dry Index analysis based on empirical mode decomposition
Zeng, Qingcheng
Qu, Chenrui
MARITIME POLICY & MANAGEMENT, 2014, 41 (03) : 224 - 240
[3] Rainfall Forecasting Based on Ensemble Empirical Mode Decomposition and Neural Networks
Beltran-Castro, Juan
Valencia-Aguirre, Juliana
Orozco-Alzate, Mauricio
Castellanos-Dominguez, German
Travieso-Gonzalez, Carlos M.
ADVANCES IN COMPUTATIONAL INTELLIGENCE, PT I, 2013, 7902 : 471 - 480
[4] Forecasting the Baltic Dry Index by using an artificial neural network approach
Sahin, Bekir
Gurgen, Samet
Unver, Bedir
Altin, Ismail
TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, 2018, 26 (03) : 1673 - 1684
[5] Forecasting Volatility of Chinese Composite Index Based on Empirical Mode Decomposition and Neural Network
Xu, Jingfeng
Liu, Jian
ECONOMICS AND FINANCE RESEARCH, 2011, 4 : 227 - +
[6] Stock index forecasting based on multivariate empirical mode decomposition and temporal convolutional networks
Yao, Yuan
Zhang, Zhao-yang
Zhao, Yang
APPLIED SOFT COMPUTING, 2023, 142
[7] Forecasting stochastic neural network based on financial empirical mode decomposition
Wang, Jie
Wang, Jun
NEURAL NETWORKS, 2017, 90 : 8 - 20
[8] Forecasting tourism demand based on empirical mode decomposition and neural network
Chen, Chun-Fu
Lai, Ming-Cheng
Yeh, Ching-Chiang
KNOWLEDGE-BASED SYSTEMS, 2012, 26 : 281 - 287
[9] A hybrid model for wind power forecasting based on ensemble empirical mode decomposition and wavelet neural networks
Wang, He
Hu, Zhijian
Chen, Zhen
Zhang, Menglin
He, Jianbo
Li, Chen
Diangong Jishu Xuebao/Transactions of China Electrotechnical Society, 2013, 28 (09): : 137 - 144
[10] Wind Power Forecasting using Hybrid Recurrent Neural Networks with Empirical Mode Decomposition
van Heerden, Liaan
Vermeulen, H. J.
van Staden, Chantelle
2022 IEEE INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING AND 2022 IEEE INDUSTRIAL AND COMMERCIAL POWER SYSTEMS EUROPE (EEEIC / I&CPS EUROPE), 2022,

← 1 2 3 4 5 →