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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/modeling-time-series-and-sequential-data
课程评论：没有评论
课程名称：时间序列与序列数据建模 课程概述： 本课程旨在教授学员建立、改进、外推和解释针对单一顺序系列的模型。课程介绍了三种建模方法。第一部分介绍传统的Box-Jenkins方法，重点是时间序列建模。课程从平稳数据的模型（ARMA）入手，探讨趋势和季节性的模型（ARIMA），并最终介绍在ARIMAX模型中指定传递函数组件的信息。接下来，课程转向贝叶斯时间序列建模，扩展基本贝叶斯框架，以适应数据中的自回归变异以及动态输入变量的影响。最后，课程介绍了用于时间序列的机器学习算法，特别是梯度提升和递归神经网络算法，适合处理数据中的非线性关系。通过实例，帮助学员直观理解这些算法的有效使用。 课程最后探讨如何通过结合不同方法的优势来提高预测精度。最后一课包括创建组合（或集成）和混合模型预测的演示。 适合对象： 本课程适合希望利用分析工具来扩展其机器学习技能的分析师，特别是那些涉及时间变量收集的数据的分析、修改、建模、预测和管理。 软件工具： 课程使用多种不同的软件工具。熟悉Base SAS、SAS/ETS、SAS/STAT和SAS Visual Forecasting，以及用于处理和建模序列数据的开源工具是有帮助的，但不是必需的。贝叶斯分析和机器学习模型的课程内容假设学员具备一定的基础知识。学生可以通过完成SAS教育课程《使用SAS进行贝叶斯分析》和《使用SAS Viya进行机器学习》来获得相关背景知识。 课程大纲： 1. 专业概述（回顾） - 课程概述和预期内容。 2. 课程概览 - 了解课程内容和SAS Viya 的使用。 3. 时间序列导论 - 复习时间序列的基本概念和简单模型规格。 4. ARIMAX模型 - 传统稳定数据模型及其扩展到趋势和季节性变体。 5. 贝叶斯时间序列分析 - 结合时间序列和贝叶斯分析的基本概念。 6. 机器学习时间序列建模方法 - 使用SAS机器学习工具进行时间序列预测。 7. 混合建模方法与外部预测 - 结合外部预测与内生成组合预测以提高精度。 8. 课程回顾 - 总结和回顾所学内容。 课程将帮助分析师掌握时间序列分析的前沿技术和实践应用。
Part: 1
Title:Specialization Overview (Review)
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 get an idea of the scope of this course and learn to use SAS Viya for Learners to do the practices in the course.
Part: 3
Title:Introduction to Time Series
Description:This module reviews fundamental time series ideas. You learn about the basic components of systematic variation in time series data and some simple model specifications, such as the autoregressive order one and the random walk. You also learn about Exponential smoothing models or ESMs, selecting a champion ESM, and generating forecasts on time series.
Part: 4
Title:ARIMAX Models
Description:This module has four parts. The first part describes traditional models for stationary data: Auto Regressive Moving Average or ARMA models. The second part describes how the ARMA framework is generalized to accommodate trend variation. This involves integration, and results in the ARIMA model. The third part describes how the ARIMA model is adapted to handle seasonal variation in the data. The fourth and final part of the module introduces the dynamic regression or ARIMAX model and describes concepts related to identifying transfer function components and specifying ARIMAX models.
Part: 5
Title:Bayesian Time Series Analysis
Description:In this module, we combine the worlds of time series and Bayesian analysis. We begin with a brief review of Bayesian analysis. We then explore how to incorporate autoregressive, seasonal, and exogenous components in a Bayesian time series. We conclude with a discussion on Bayesian scoring and posterior predictive distributions.
Part: 6
Title:Machine Learning Approaches to Time Series Modeling
Description:In this module you learn how to use SAS machine learning tools to forecast individual time series. You learn to prepare the time series data for use with the machine learning tools, and how to build and score forecasting models using these tools. We focus on gradient boosting and recurrent neural network models and discuss when it would be useful to use these methods.
Part: 7
Title:Hybrid Modeling Approaches and External Forecasts
Description:This module describes how forecasts that are generated externally to the forecasting system can be accommodated in SAS Visual Forecasting. We'll use external forecasts to create a combined or ensemble forecast that has the potential to improve forecast precision relative to the constituent, external forecasts. This module concludes with a discussion of hybrid model forecasts that combine traditional and machine learning approaches to forecasting.
Part: 8
Title:Course Review
Description:
In this course you learn to build, refine, extrapolate, and, in some cases, interpret models designed for a single, sequential series. There are three modeling approaches presented. The traditional, Box-Jenkins approach for modeling time series is covered in the first part of the course. This presentation moves students from models for stationary data, or ARMA, to models for trend and seasonality, ARIMA, and concludes with information about specifying transfer function components in an ARIMAX, or time series regression, model. A Bayesian approach to modeling time series is considered next. The basic Bayesian framework is extended to accommodate autoregressive variation in the data as well as dynamic input variable effects. Machine learning algorithms for time series is the third approach. Gradient boosting and recurrent neural network algorithms are particularly well suited for accommodating nonlinear relationships in the data. Examples are provided to build intuition on the effective use of these algorithms. The course concludes by considering how forecasting precision can be improved by combining the strengths of the different approaches. The final lesson includes demonstrations on creating combined (or ensemble) and hybrid model forecasts. This course is appropriate for analysts interested in augmenting their machine learning skills with analysis tools that are appropriate for assaying, modifying, modeling, forecasting, and managing data that consist of variables that are collected over time. This course uses a variety of different software tools. Familiarity with Base SAS, SAS/ETS, SAS/STAT, and SAS Visual Forecasting, as well as open-source tools for sequential data handling and modeling, is helpful but not required. The lessons on Bayesian analysis and machine learning models assume some prior knowledge of these topics. One way that students can acquire this background is by completing these SAS Education courses: Bayesian Analyses Using SAS and Machine Learning Using SAS Viya.
