|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/a-complete-guide-to-time-series-analysis-forecasting-in-r/
课程评论：没有评论
**课程名称：** R语言时间序列分析与预测完全指南 **课程概要：** 本课程是一门面向初学者的R语言时间序列分析与预测入门课程，无需R语言或数据科学基础。课程内容侧重于实际应用，而非理论和数学推导，包含大量实例和练习，提供完整的R代码和数据集供学员实践。 **学习目标：** 完成本课程后，您将能够： * 在R中探索和可视化时间序列数据。 * 应用和解读时间序列回归结果。 * 理解各种时间序列预测方法。 * 使用通用的预测工具和模型处理不同的预测场景。 * 利用统计程序在经济、商业和社会科学领域计算、可视化和分析时间序列数据。 **主要学习内容：** * R语言时间序列数据的探索与可视化 * 时间序列预测的基准方法 * 时间序列预测的准确性评估 * 线性回归模型 * 指数平滑法 * 平稳性检验（ADF、KPSS）、差分等概念 * ARIMA、SARIMA和ARIMAX（动态回归）模型 * 其他预测模型 **课程特色：** * 强调应用，注重实践。 * 提供详尽的R代码和数据集。 * 内容自成体系，易于理解。 * 包含丰富的实例和练习，巩固学习效果。
Forecasting involves making predictions. It is required in many situations: deciding whether to build another power generation plant in the next ten years requires forecasts of future demand; scheduling staff in a call center next week requires forecasts of call volumes; stocking an inventory requires forecasts of stock requirements. Forecasts can be required several years in advance (for the case of capital investments) or only a few minutes beforehand (for telecommunication routing). Whatever the circumstances or time horizons involved, forecasting is an essential aid to effective and efficient planning. This course provides an introduction to time series forecasting using R.No prior knowledge of R or data science is required.Emphasis on applications of time-series analysis and forecasting rather than theory and mathematical derivations.Plenty of rigorous examples and quizzes for an extensive learning experience.All course contents are self-explanatory.All R codes and data sets and provided for replication and practice.At the completion of this course, you will be able toExplore and visualize time series data.Apply and interpret time series regression results.Understand various methods to forecast time series data.Use general forecasting tools and models for different forecasting situations.Utilize statistical programs to compute, visualize, and analyze time-series data in economics, business, and the social sciences. You will learnExploring and visualizing time series in R.Benchmark methods of time series forecasting.Time series forecasting forecast accuracy.Linear regression models.Exponential smoothing.Stationarity, ADF, KPSS, differencing, etc.ARIMA, SARIMA, and ARIMAX (dynamic regression) models.Other forecasting models.
