Master Time Series Forecasting with R: Analyze & Predict

Instructor: EDUCBA

Access provided by EDGE Group

3 modules

Gain insight into a topic and learn the fundamentals.

9 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

9 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

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.

Skills you'll gain

Tools you'll learn

R Programming

Details to know

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Assessments

11 assignments

Taught in English

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There are 3 modules in this course

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.

What's included

12 videos4 assignments

12 videos Total 90 minutes

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

4 assignments Total 60 minutes

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

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.

What's included

12 videos4 assignments

12 videos Total 108 minutes

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

4 assignments Total 60 minutes

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

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.