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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/time-series-features
课程评论：没有评论
课程名称：时间序列数据特征创建 课程概述：本课程专注于时间序列数据的探索、特征创建和特征选择。讨论的主题包括时间序列的分箱、平滑、变换和数据集操作，以及谱分析、奇异谱分析、距离度量和模式分析。课程教你如何执行模式分析并在频率域实现分析，了解距离度量的工作原理，实施应用，探索信号成分，并创建时间序列特征。 本课程适合具有定量背景的分析师以及希望丰富时间序列工具箱的领域专家。在参加本课程之前，你应该对基本统计概念感到舒适。你可以通过完成SAS统计课程获得这种经验。对矩阵和主成分分析的熟悉程度也会有帮助，但不是必需的。 课程大纲： 1. **专业概述**：了解该专业中的所有课程及其期望内容。 2. **课程概述**：学习本课程的范围，并访问将在课程中使用的软件下载和文件。 3. **时间序列基础**：了解如何将事务序列转换为时间序列，探索通过分解和分箱分析时间序列中的信号成分，以及创建新的时间序列特征。 4. **距离度量**：学习时间序列之间的距离或相似性度量的实用性，计算的距离度量将作为两种分析的基础。 5. **谱分析和奇异谱分析（SSA）**：讨论并展示频率域分析的基本概念和应用，以及奇异谱分析的演示。 6. **模式分析**：学习如何检测时间序列中的模式及其用途。 7. **课程回顾**：对课程内容进行总结和复习。
Part: 1
Title:Specialization Overview
Description:In this module you get an overview of the courses in this specialization and what you can expect.
Part: 2
Title:Course Overview
Description:In this module you learn about the scope of this course and you access the software and files you will use for practices in the course.
Part: 3
Title:Time Series Basics
Description:In this module, you learn about converting transactional sequences to time series. Other topics include exploring signal components in time series via decompositions and binning, and creating new time series features.
Part: 4
Title:Distance Measures
Description:In this module you learn about the usefulness of distance or similarity measures between time series. Calculated distance measure are used as the basis in two analyses.
Part: 5
Title:Spectral Analysis and Singular Spectrum Analysis (SSA)
Description:In this module, we discuss and illustrate the basic ideas and applications in frequency domain analysis. We also discuss SSA and present demonstrations of applied SSA.
Part: 6
Title:Motif Analysis
Description:In this module you learn about detecting motifs in times series and their usefulness.
Part: 7
Title:Course Review
Description:
This course focuses on data exploration, feature creation, and feature selection for time sequences. The topics discussed include binning, smoothing, transformations, and data set operations for time series, spectral analysis, singular spectrum analysis, distance measures, and motif analysis. In this course you learn to perform motif analysis and implement analyses in the spectral or frequency domain. You also discover how distance measures work, implement applications, explore signal components, and create time series features. This course is appropriate for analysts with a quantitative background as well as domain experts who would like to augment their time-series tool box. Before taking this course, you should be comfortable with basic statistical concepts. You can gain this experience by completing the Statistics with SAS course. Familiarity with matrices and principal component analysis are also helpful but not required.
