【作者】 艾玲；

【导师】 李明；

【作者基本信息】 华东师范大学 ， 电路与系统， 2010， 硕士

【摘要】 时间序列是指按照时间顺序取得的一系列观测值,很多数据是以时间序列的形式出现的,从经济、金融到工程技术,从天文、地理到气象,从医学到生物等各个领域都涉及到时间序列,例如船舶运动,每天的气温,公路事故数量的周度序列等等。在自然科学和社会科学各研究领域中,大量决策问题都离不开预测,时间序列预测是指利用时间序列的若干历史观测值来预测未来某时刻的取值。时间序列预测的应用非常广泛,如天气预报,股价指数的预测,船舶运动的极短期预报等等。本文第一章介绍了该课题的背景意义以及时间序列预测的国内外研究现状；在第二章中给出了五种常用的时间序列预测方法基本原理的详细说明,包括线性预测方法(自回归模型AR、自回归滑动平均模型ARMA、求和自回归滑动平均模型ARIMA)和非线性预测方法(径向基函数RBF神经网络和BP网络)。第三章论文在MATLAB环境下实现了这五种方法用于海浪信号的预测,并且比较了这些方法预测后的四种误差,包括偏差、归一化偏差、均方根误差、标准偏差。同时,比较不同情况下这些方法预测的效果,分别是：预测步长增加时；取不同的时间段进行预测时,预测误差的大小等；比较了这些方法用于均匀分布和高斯分布随机数据序列的预测效果。第四章在MATLAB的GUIDE平台上开发了一个时间序列短期预测系统,涵盖了以上五种预测方法,该系统可以方便的对时间序列进行预测,可自由选择预测方法,待预测的数据和预测步长,能直观地显示预测结果以及常用的四种误差值,便于用户对这些方法的预测效果进行评估分析。最后,第五章总结了以上五种预测方法的预测性能并对方法的改进之处寄予展望。本文的主要贡献有：(1)在MATLAB环境下实现了五种常用的时间序列预测方法,并使用这些方法对海浪信号、均匀分布和高斯分布数据进行了短期预测。(2)比较这五种预测方法的预测步长,稳定性,预测精度和运行时间,总结了它们的优缺点。(3)在MATLAB的GUIDE平台上开发了时间序列短期预测系统,方便使用五种方法对时间序列进行预测。更多还原

【Abstract】 Time series is a series of observations obtained according to the time order. A lot of data is in the form of time series. From the economic, finance to engineering, from astronomy, geography to meteorology, from medicine to biology, and so on. All of these areas are related to time-series. For example, ship motion, the daily temperature, and the sequence weekly of the number of road accidents and so on. In the research fields of natural sciences and social sciences, a large number of decision-making can not be separated from prediction. Time series forecasting refers to the use of the historical observations of time series to predict the value at a future time. Time series forecasting is widely used, such as the weather forecasts, stock price index forecasts, extremely short-time prediction of ship motion and so on.In chapter 1 this paper introduces the research background of time series forecasting and the status at home and abroad. Chapter 2 presents detailed description of the basic theories on the five kinds of commonly used time series forecasting methods, including the linear forecasting methods (auto-regressive model AR, auto-regressive moving average model ARMA, and integrated autoregressive moving average model ARIMA) and the non-linear forecasting methods (radial basis function RBF neural network and BP network). In chapter 3, firstly, we implement these five kinds of methods for forecasting a wave signal, and experimentally compare the four kinds of error on these five forecasting methods, including bias, normalized bias, root mean square error and standard deviation. Furthermore, we compare the forecasting performances of these methods in different circumstances. Namely, when the prediction step increases, when take a different time period for forecasting, the magnitude of error for these five methods. Finally, we compare their performance on forecasting of the Uniform distribution and Gaussian distribution random sequence. In chapter 4, we develop a time series short-term forecasting system in the MATLAB GUIDE development platform, it covers the five kinds of forecasting methods above and we can easily predict time series on it. We are free to choose forecasting methods, data for forecasting and the prediction step, then the system can visually indicate the forecasting results and four kinds of error commonly used, it helps us in assessment and analysis of these forecasting methods. Finally, Chapter 5 summarizes the performances of these five forecasting methods and gives an outlook for their improvements.The main contributions of this paper are:(1) five kinds of time series forecasting methods are implemented in the MATLAB environment, and these methods are used on the short-term prediction on a wave signal and sequences satisfies Uniform distribution and Gaussian distribution. (2) make a comparison to the prediction steps, stability, prediction accuracy and running time of the five forecasting methods, summarize their advantages and disadvantages. (3) develop a short-term time series forecasting system in the MATLAB GUIDE development platform, easily use the five forecasting methods for time series forecasting.更多还原