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时间序列分析

Analysis of Time Series
课程网址: http://videolectures.net/acai05_belazzi_ats/  
主讲教师: Riccardo Bellazzi
开课单位: 帕维亚大学
开课时间: 2007-02-25
课程语种: 英语
中文简介:
时间序列的研究是智能数据分析的一个重要方面。这个领域非常广泛,并且已经用非常不同的方法处理过,从微分方程到随机模型,再到人工智能系统。本课程将介绍时间序列分析作为建模动力系统的一般问题的一部分。系统框架;理论将提供对这类问题的一般看法,它将使我们能够连贯地概述大多数时间序列分析方法。更详细地说,系统理论的原则将首先被讨论;动力系统的概念将进行研究,并给出一些系统论的结果。将讨论状态、平衡、线性、可观察性和可达性的概念。然后将介绍一些建模工具,从黑匣子到结构模型。本文将简要介绍随机线性和非线性模型,包括AR、MA和ARMAX模型。此外,还将介绍一种从输入/输出数据中获取结构信息的方法。本课程最后将展示如何在时间序列聚类问题中有效地利用系统动力学知识。将重新讨论基于距离、基于模型和基于模板的系统动力学信息。
课程简介: The study of time series is an essential aspect of Intelligent Data Analysis. The field is very broad, and it has been treated with very different methodological approaches, ranging from differential equations to stochastic models and to AI-based systems. The lesson will present time series analysis as a part of the general problem of modelling dynamical systems. The framework of systems’ theory will provide a general view of such problem, and it will permit to coherently overview the majority of the time series analysis approaches. In more detail, the principles of systems theory will be first discussed; the concept of “dynamical system” will be investigated and some results of systems theory will be presented. The notions of state, equilibrium, linearity, observability and reachability will be discussed. Some modelling tools will be then introduced, ranging from black-box to structural models. Stochastic linear and non linear models will be briefly described, including AR, MA, and ARMAX models. Moreover, a method to obtain structural information from input/output data will be introduced. The lesson will finally show how the knowledge on systems dynamics can be effectively exploited in the time series clustering problem. Distance-based, model-based and template-based will be revisited in order to account for information on the systems dynamics.
关 键 词: 时间; 序列; 分析
课程来源: 视频讲座网
最后编审: 2019-10-31:lxf
阅读次数: 101