Master Time Series Forecasting with R: Analyze & Predict

位教师：EDUCBA

访问权限由 New York State Department of Labor 提供

3个模块

深入了解一个主题并学习基础知识。

8 小时完成

灵活的计划

自行安排学习进度

3个模块

深入了解一个主题并学习基础知识。

8 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Define forecasting fundamentals and classify methods for time-dependent data.
Apply regression, decomposition, and exponential smoothing in R.
Implement ARIMA and SARIMA models with ACF/PACF diagnostics for accuracy.

您将获得的技能

要了解的详细信息

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作业

11 项作业

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

该课程共有3个模块

By the end of this course, learners will be able to define the fundamentals of forecasting, classify forecasting methods, apply regression and decomposition techniques, and implement advanced models like ARIMA and SARIMA to accurately predict time-dependent data.

This comprehensive course equips participants with the tools to tackle real-world forecasting challenges using R. Beginning with the foundations of business analytics forecasting, learners will explore methods, steps, and common pitfalls before moving into practical applications of simple forecasting models. The course then advances into regression-based forecasting, covering simple, multiple, and non-linear regression, while also integrating predictors and lagged variables for more reliable time series analysis. Finally, learners will gain hands-on expertise with exponential smoothing, ARIMA, and Seasonal ARIMA modeling, supported by ACF and PACF diagnostics. What makes this course unique is its step-by-step progression from basics to advanced forecasting, its practical use of R for implementation, and its focus on both interpretability and accuracy. By completing this program, learners will be prepared to design robust forecasting solutions that improve decision-making in business, finance, operations, and beyond.

This module introduces learners to the fundamental principles of forecasting within the field of business analytics. It explains the purpose and scope of forecasting, explores different forecasting methods, and highlights common challenges businesses face when predicting future trends. Learners will also gain practical insights into simple forecasting approaches, transformations, and accuracy evaluation techniques, building a strong foundation for advanced forecasting models.

涵盖的内容

12个视频4个作业

12个视频总计90分钟

Introduction to Business Analytics Forecasting 3分钟
What is Forecasting 10分钟
What is Forecasting Continues 6分钟
Methods of Forecasting 8分钟
Steps of Forecasting 11分钟
Problems with Forecasting 7分钟
Simple Forecasting Methods 9分钟
Methods in Simple Forecasting Methods 7分钟
Example of Simple Forecasting Methods 7分钟
Transformations and Adjustments 6分钟
Transformations and Adjustments Example 7分钟
Forecasting Accuracy 8分钟

4个作业总计60分钟

Introduction to Forecasting 10分钟
Forecasting Methods and Challenges 10分钟
Basics of Simple Forecasting 10分钟
Graded-Foundations of Forecasting 30分钟

This module explores how regression techniques and decomposition methods can be applied to time series forecasting. Learners will gain an in-depth understanding of simple, multiple, and non-linear regression, the use of predictors and lagged variables, and the unique considerations of time series regression. The module also introduces decomposition approaches to separate time series into trend, seasonal, cyclical, and irregular components, helping learners build accurate and interpretable forecasting models in R.

涵盖的内容

12个视频4个作业

12个视频总计108分钟

Simple Regression in Forecasting 10分钟
Simple Regression in Forecasting Continues 10分钟
Example of Simple Regression in Forecasting 12分钟
Non Linear Regression 8分钟
Forecasting with Regression 10分钟
Time Series Regression 6分钟
Time Series Regression Continues 7分钟
Multiple Linear Regression 7分钟
Predictors Forecasting for Formula 12分钟
Time Series Decomposition 9分钟
Time Series Decomposition Continues 9分钟
Forecasting with Decomposition 9分钟

4个作业总计60分钟

Regression in Forecasting 10分钟
Time Series Regression and Predictors 10分钟
Decomposition Approaches 10分钟
Graded-Regression and Decomposition in Time Series 30分钟

This module focuses on advanced time series forecasting techniques, including exponential smoothing, ARIMA, and Seasonal ARIMA models. Learners will explore the theoretical foundations and practical applications of autoregressive and moving average models, understand the role of ACF and PACF in model selection, and learn how to handle seasonal and non-seasonal time series data. By mastering these advanced methods, learners will be able to build robust and accurate forecasting models in R that address both short-term fluctuations and long-term seasonal trends.