基于小波分析和ARIMA模型的中国出口集装箱运价指数预测

发布时间：2018-04-09 09:27

本文选题：中国出口集装箱运价指数　切入点：小波分析　出处：《大连海事大学》2013年硕士论文

【摘要】：作为国际贸易的派生市场,集装箱班轮运输市场受世界经济的影响十分严重,后金融危机时期的集装箱班轮运输市场运价备受关注。由于集装箱运输市场受到多种因素的影响,由中国出口集装箱运价指数(CCFI)的历史数据构成的时间序列信号中含有很多噪声。因此,本文将小波分析和ARIMA模型相结合,运用小波分析提取运价指数信号中的低频成分,并剔除高频成分,从而使得ARIMA模型能够更好地进行拟合及预测。本文开篇阐述了论文的研究背景、研究意义、国内外研究现状以及研究的主要内容和方法。其次分别从集装箱运输市场格局、集装箱运输船舶运营、集装箱运输航线布局以及集装箱运价概述等四个方面分析了集装箱运输市场的发展与现状。接下来简单地介绍了国内外运价指数及中国出口集装箱运价指数(CCFI)。然后介绍了小波理论的发展足迹与小波分析的理论基础,随后对中国出口集装箱运价指数(CCFI)的原始数据进行了去噪处理。紧接着介绍了时间序列及其相关概念以及时间序列模型,继而根据经过小波分析并去噪处理后的中国出口集装箱运价指数(CCFI)的时间序列数据,建立了ARIMA模型,对中国出口集装箱运价指数(CCFI)进行了预测,并对预测结果进行了分析。最后对全文进行了总结,针对不足之处,对今后的研究进行了展望。根据经过小波分析并处理后的中国出口集装箱运价指数(CCFI)的时间序列数据,建立ARIMA模型,有效地提升了ARIMA模型的拟合度及预测效果。本文的研究将对船公司、货主、代理等航运相关者了解集装箱班轮运输市场形势,判断市场走向,及时做出相应调整,规避市场风险提供有益的参考。
[Abstract]:As a derivative market of international trade, the container liner transportation market is seriously affected by the world economy.Because the container transportation market is affected by many factors, the time series signal composed of the historical data of China's export container freight index CCFI contains a lot of noise.Therefore, this paper combines wavelet analysis with ARIMA model, using wavelet analysis to extract the low frequency component of the freight rate index signal, and eliminating the high frequency component, so that the ARIMA model can fit and predict better.At the beginning of this paper, the research background, research significance, domestic and foreign research status, main contents and methods of research are described.Secondly, the development and present situation of container transport market are analyzed from four aspects: container transport market pattern, container shipping vessel operation, container shipping route layout and container freight rate overview.Then it briefly introduces the domestic and foreign freight price index and China's export container price index CCFI.Then, the development footprint of wavelet theory and the theoretical basis of wavelet analysis are introduced, and then the original data of China Export Container Freight Index (CCFI) are de-noised.Then it introduces the time series and its related concepts as well as the time series model. Then according to the time series data of China's export container price index (CCFI) after wavelet analysis and de-noising, the ARIMA model is established.This paper forecasts China's export container price index (CCFI) and analyzes the forecast results.In the end, the paper summarizes the whole paper and looks forward to the future research aiming at the deficiency.According to the time series data of China Export Container Freight Index (CCFI) after wavelet analysis, the ARIMA model is established, which can effectively improve the fit degree and prediction effect of the ARIMA model.The research in this paper will provide a useful reference for shipping companies, shippers, agents and other shipping related parties to understand the market situation of container liner transportation, judge the market trend, make corresponding adjustments in time, and avoid market risks.
【学位授予单位】：大连海事大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：U695.22

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