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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/large-scale-forecasting-sas-viya
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课程名称：构建大规模自动化预测系统 课程概述：本课程教授如何使用SAS Visual Forecasting工具开发和维护大规模预测项目。初期重点在于选择适当的数据创建方法和变量转化、模型生成及模型选择。随后，学习如何通过修改系统中的默认流程来提高整体基线预测性能。该课程适合希望增强其机器学习技能的分析师，特别是对适用于分析、修改、建模、预测和管理随着时间收集的变量数据的工具感兴趣的人员。由于课程主要基于语法，因此参加者需具备一定的编码基础，有面向对象语言的经验和处理大型表格的熟悉度将更为有益。 课程大纲： 1. 专业领域概述：介绍该专业领域课程及期望。 2. 课程概述：本模块概述课程内容。 3. 大规模预测介绍：概述用于解决大规模预测问题的ATSM包中的对象和方法。 4. 探索与处理时间戳数据：使用TSMODEL过程执行时间序列累积和缺失值解释，并讨论时间序列层次结构及BY语句的使用。 5. 自动预测：模型规范与选择：使用ATSM包进行自动预测、模型选择和规范。 6. 创建自定义模型和管理模型列表：提供逐步指导，构建自定义规范并将模型纳入自动模型选择流程。 7. 预测系统中的事件变量：生成事件变量的多种方式，并选择冠军模型生成自动预测。 8. 统计预测的协调：描述协调过程及其工具和选项。 9. 设置预测系统并生成最佳预测：讨论提高预测精度的最佳实践和系统工具。 10. 课程复习：测试对课程材料的理解。 该课程帮助分析师掌握开发和优化大型预测系统的技能，适合希望在数据建模和时间序列预测领域深化知识的学员。
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. Note: This same module appears in each course in this specialization.
Part: 2
Title:Course Overview
Description:
Part: 3
Title:Introduction to Large-Scale Forecasting
Description:In this modules you'll get an overview of the functionality used in the course. We'll describe how objects and methods in the Automatic Time Series Modeling, or ATSM, package in SAS Visual Forecasting can be combined to solve the large-scale forecasting problem. We'll also describe how the configuration of objects and information flows change depending on what stage of the automatic forecasting process you are in.
Part: 4
Title:Exploring and Processing Timestamped Data
Description:In this module we'll use the TSMODEL procedure to perform time series accumulation and missing value interpretation. We'll use packages for PROC TSMODEL, which are blocks of code that can be inserted within the flow of your PROC TSMODEL code to perform specialized tasks for both data preparation and analysis. Then, we'll discuss time series hierarchies and how to use a BY statement in PROC TSMODEL to create a hierarchy.
Part: 5
Title:Automatic Forecasting: Model Specification and Selection
Description:In this module, we'll use the ATSM package in PROC TSMODEL to perform automatic forecasting, model selection, and specification. We'll walk through the process for declaring and using the many different ATSM objects and discuss how and where each object fits within the automatic forecasting process.
Part: 6
Title:Creating Custom Models and Managing Model Lists
Description:This module describes and illustrates functionality for creating your own custom models in the forecasting system. We'll provide step-by-step instructions for building a custom specification and then modifying the automatic model selection process to include your model as a candidate for all series in a given level of the data hierarchy.
Part: 7
Title:Event Variables in the Forecasting System
Description:In this module, we'll generate event variables three different ways. First, we'll use the ATSM package to create and implement predefined event variables. Second, we'll create event variables using the HPFEVENTS procedure. Third, we'll perform conditional BY-group processing for event variable creation. Next, we'll use and identify ARIMAX and ESM models, produce model selection lists, and select a champion model. Using the selected champion model and passing the predefined event variables to the TSMODEL procedure, we'll generate automatic forecasts and output model estimates and fit statistics.
Part: 8
Title:Reconciling Statistical Forecasts
Description:Reconciling statistical forecasts occurs after the automatic model generation, selection, and forecasting processes are done. In this module, we describe the reconciliation process and illustrate system tools and options for reconciling statistical forecasts we generated earlier in the course.
Part: 9
Title:Setting Up the Forecasting System and Generating Best Forecasts
Description:This module covers a variety of topics. First, we'll discuss system tools and best practices that have the potential to improve the precision of your system forecasts. These include best practices like honest assessment for champion model selection and system tools like outlier detection and combined model forecasts. Next, we'll describe options and best practices associated with rolling the system forward in time.
Part: 10
Title:Course Review
Description:In this module you test your understanding of the course material.
In this course you learn to develop and maintain a large-scale forecasting project using SAS Visual Forecasting tools. Emphasis is initially on selecting appropriate methods for data creation and variable transformations, model generation, and model selection. Then you learn how to improve overall baseline forecasting performance by modifying default processes in the system. 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. The courses is primarily syntax based, so analysts taking this course need some familiarity with coding. Experience with an object-oriented language is helpful, as is familiarity with manipulating large tables.
