|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/excel-business-forecasting-time-series
课程评论：没有评论
课程名称：Excel时间序列模型商业预测 概述：本课程探讨了不同的时间序列商业预测方法，涵盖了适用于时间序列数据中不同成分（水平、趋势和季节性）的多种商业预测技术。学习者将掌握理论方法，并运用Microsoft Excel对商业数据进行实际应用。课程将这些预测方法编程到Excel中，通过图形方式展示，并优化这些模型以产生准确的预测。课程还将比较不同模型及其预测，帮助决策选择最适合企业需求的模型。 课程大纲： 1. 欢迎与重要信息： 本模块介绍商业预测对任何组织的重要性，强调没有商业预测就无法规划资源、生产和供应链以及最终的成本、收入和利润。课程将重点关注如何利用时间序列数据集理解数据背后的不同成分，并在此基础上应用适当的模型。 2. 时间序列模型： 本模块探讨商业预测的背景和目的，介绍三种商业预测类型——时间序列、回归和判断预测，重点学习时间序列模型及其数据成分。 3. 水平时间序列： 探索适用于水平数据的不同时间序列预测方法。 4. 趋势时间序列： 探索适用于趋势数据的不同时间序列预测方法。 5. 季节性时间序列： 探索适用于季节性数据的时间序列预测方法（如Winters指数平滑法）。 6. 分解模型： 探索用于季节性数据的时间序列预测方法（分解）。 这个课程为希望提高商业预测能力的个人或企业提供了全面的工具和技能，结合理论和实践，旨在提升学生在商业环境中的数据分析能力。
Name:Welcome and Critical Information
Description:Business Forecasting is part of any and every organisation. Organisations need to forecast so that they can plan for the organisation’s needs. Business forecasts are the inputs to every organisation’s planning – without business forecasts we cannot plan for our resources, our production, our supply chains – and ultimately our costs, revenues and profits. The current state of the world makes business forecasting even more fundamental to the operation of institutions. In this course we focus on Excel Skills for Business Forecasting using Time Series Models. We will be looking at how your business can utilise time series data sets to understand the different components underlying this data, and then apply the relevant model depending on these components. We will look at a range of business forecasting methods, and sometimes, more than one method may be needed! The models we look at are: Naïve Forecasting, Moving Averages, Trend-fitting, Simple Exponential Smoothing, Holt’s Exponential Smoothing, Winters Exponential Smoothing, and Decomposition. This course then continues in our second course in this specialisation which looks at Regression Models, and our third course in this specialisation which looks at Judgmental Forecasting. #EveryoneSayWow
Name:Time Series Models
Description:In this module, we explore the context and purpose of business forecasting and the three types of business forecasting — time series, regression, and judgmental. This course focuses on time series models. We will learn about time series models, as well as the component of time series data. We will then look at a preliminary forecasting method — Average Forecasts. Once we have a forecast, we need a tool to judge the accuracy of the forecasts — which are the forecasts and the error criterion calculated from these.
Name:Level Time Series
Description:In this module, we explore different time series forecasting methods available for data that is level.
Name:Trending Time Series
Description:In this module, we explore different time series forecasting methods available for data that is trending.
Name:Seasonal Time Series
Description:In this module, we explore a time series forecasting method (Winters Exponential Smoothing) available for data that is seasonal.
Name:Decomposition
Description:In this module, we explore a time series forecasting method (Decomposition) available for data that is seasonal.
This course explores different time series business forecasting methods. The course covers a variety of business forecasting methods for different types of components present in time series data — level, trending, and seasonal. We will learn about the theoretical methods and apply these methods to business data using Microsoft Excel. These forecasting methods will be programmed into Microsoft Excel, displayed graphically, and we will optimise these models to produce accurate forecasts. We will compare different models and their forecasts to decide which model best suits our business' needs.
