Forecasting Models & Time Series Analysis for Business in R
Forecasting Models & Time Series Analysis for Business in R, available at $79.99, has an average rating of 4.61, with 146 lectures, based on 317 reviews, and has 2088 subscribers.
You will learn about Gain a comprehensive understanding of time series analysis and forecasting models through hands-on practice and real-world applications Implement forecasting models and time series analysis in a business environment to improve performance and efficiency. Understand and apply various forecasting models, including Prophet and ARIMA, to make informed business decisions. Apply data science and analytics principles to real-world business scenarios through hands-on practice in R. Develop proficiency in using R programming for time series analysis in business settings. Improve demand planning and forecasting abilities by utilizing time series analysis techniques. Learn to analyze and interpret time series data to make predictions about future trends and patterns. Utilize R programming to create visualizations and data visualizations to better understand time series data. Understand the importance of forecasting models in business operations and decision-making. Learn to identify and diagnose common problems and limitations in time series analysis. This course is ideal for individuals who are Business and data analysts looking into learning about Forecasting or Finance professionals wanting to modernize their forecasting proccesses or General data-driven professionals who would like to learn about Forecasting or Marketing experts interested in finding patterns in sales data It is particularly useful for Business and data analysts looking into learning about Forecasting or Finance professionals wanting to modernize their forecasting proccesses or General data-driven professionals who would like to learn about Forecasting or Marketing experts interested in finding patterns in sales data.
Enroll now: Forecasting Models & Time Series Analysis for Business in R
Summary
Title: Forecasting Models & Time Series Analysis for Business in R
Price: $79.99
Average Rating: 4.61
Number of Lectures: 146
Number of Published Lectures: 146
Number of Curriculum Items: 146
Number of Published Curriculum Objects: 146
Original Price: €219.99
Quality Status: approved
Status: Live
What You Will Learn
- Gain a comprehensive understanding of time series analysis and forecasting models through hands-on practice and real-world applications
- Implement forecasting models and time series analysis in a business environment to improve performance and efficiency.
- Understand and apply various forecasting models, including Prophet and ARIMA, to make informed business decisions.
- Apply data science and analytics principles to real-world business scenarios through hands-on practice in R.
- Develop proficiency in using R programming for time series analysis in business settings.
- Improve demand planning and forecasting abilities by utilizing time series analysis techniques.
- Learn to analyze and interpret time series data to make predictions about future trends and patterns.
- Utilize R programming to create visualizations and data visualizations to better understand time series data.
- Understand the importance of forecasting models in business operations and decision-making.
- Learn to identify and diagnose common problems and limitations in time series analysis.
Who Should Attend
- Business and data analysts looking into learning about Forecasting
- Finance professionals wanting to modernize their forecasting proccesses
- General data-driven professionals who would like to learn about Forecasting
- Marketing experts interested in finding patterns in sales data
Target Audiences
- Business and data analysts looking into learning about Forecasting
- Finance professionals wanting to modernize their forecasting proccesses
- General data-driven professionals who would like to learn about Forecasting
- Marketing experts interested in finding patterns in sales data
How many times have you wanted to predict the future?
Welcome to the most exciting online course about Forecasting Models and Time Series in R. I will show everything you need to know to understand the now and predict the future.
Forecasting is always sexy – knowing what will happen usually drops jaws and earns admiration. On top, it is fundamental in the business world. Companies always provide Revenue growth and EBIT estimates, which are based on forecasts. Who is doing them? Well, that could be you!
WHY SHOULD YOU ENROLL IN THIS COURSE?
1 | YOU WILL LEARN THE INTUITION BEHIND THE TIME SERIES MODELS WITHOUT FOCUSING TOO MUCH ON THE MATH
It is crucial that you know why a model makes sense and the underlying assumptions behind it. I will explain to you each model using words, graphs, and metaphors, leaving math and the Greek alphabet to a minimum.
2 | THOROUGH COURSE STRUCTURE OF MOST IMPACTFUL TIME SERIES FORECASTING MODEL TECHNIQUES
The techniques in this course are the ones I believe will be most impactful, up-to-date, and sought after:
-
Holt-Winters
-
Sarimax
-
Facebook Prophet
-
Neural Networks AutoRegression
-
Ensemble approach
3 | WE CODE TOGETHER LINE BY LINE
I will guide you through every step of the way in your journey to master time series and forecasting models. I will also explain all parameters and functions that you need to use, step by step.
4 | YOU APPLY WHAT YOU ARE LEARNING IMMEDIATELY
At the end of each section regarding forecasting techniques, you are shown an exercise to apply what you learn immediately. If you do not manage? Don’t worry! We also code together line by line the solutions. The challenges range from predicting the interest in Churrasco (Brazilian BBQ) to the Wikipedia visitors of Udemy.
Did I spike your interest? Join me and learn how to predict the future!
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction to the course
Lecture 2: Course material link
Lecture 3: Course Material presentation
Lecture 4: Installing R and RStudio
Lecture 5: Getting more from the course
Lecture 6: Reviews and future of this course
Chapter 2: Introduction to Forecasting
Lecture 1: Game Plan for this section
Lecture 2: Why Forecasting?
Lecture 3: What is Time Series data?
Lecture 4: Case study briefing
Lecture 5: R – Libraries
Lecture 6: R – Loading data
Lecture 7: R – Transform Date variable
Lecture 8: R – Variable Selection
Lecture 9: R – Renaming variables
Lecture 10: R – Visualization
Lecture 11: Diogo's story: How I Learned to Program in R and Python
Chapter 3: Seasonal Decomposition
Lecture 1: Game Plan for Seasonal Decomposition
Lecture 2: Seasonal Decomposition
Lecture 3: R – Preparing Script
Lecture 4: R – Time Series Transformation
Lecture 5: R – Seasonality Plot
Lecture 6: Additive vs. Multiplicative
Lecture 7: R – Additive Decomposition
Lecture 8: R – Exercise: multiplicative decomposition
Lecture 9: Error Modelling and Stock Data
Lecture 10: Your feedback is valuable
Chapter 4: Exponential Smoothing and Holt-Winters
Lecture 1: Game Plan for Exponential Smoothing and Holt-Winters
Lecture 2: Exponential Smoothing and Holt-Winters
Lecture 3: Case Study Briefing
Lecture 4: R – Libraries and Data
Lecture 5: R – Time Series Transformation
Lecture 6: R – Time Series Plotting
Lecture 7: Training and test set
Lecture 8: R – Training and test set
Lecture 9: R – Holt-Winters model
Lecture 10: R – Holt-Winters predictions and plotting
Lecture 11: Accuracy KPIs
Lecture 12: R – Holt-Winters' accuracy assessment
Lecture 13: Holt-Winters Pros and Cons
Lecture 14: Holt-Winters Challenge
Lecture 15: R – Holt-Winters solution
Lecture 16: Diogo's story: Stakeholder Management
Chapter 5: Forecasting Product
Lecture 1: Motivation to build a Forecasting Product
Chapter 6: ARIMA, SARIMA, and SARIMAX
Lecture 1: Game Plan to build the SARIMAX model
Lecture 2: ARIMA
Lecture 3: R – Preparing Script
Lecture 4: R – Training and Test Set
Lecture 5: R – Time Series Object
Lecture 6: Pearson Correlation
Lecture 7: Auto-correlation Plots
Lecture 8: R – Auto-correlation plots
Lecture 9: Autoregressive component
Lecture 10: Integrated and Stationarity concepts
Lecture 11: R – Stationarity
Lecture 12: Moving Average Component
Lecture 13: ARIMA Factor Optimization
Lecture 14: AIC & BIC
Lecture 15: SARIMAX
Lecture 16: R – Isolating regressors
Lecture 17: R – SARIMAX model
Lecture 18: R – SARIMAX predictions, plotting, and accuracy
Lecture 19: R – Exporting Forecasts
Lecture 20: SARIMAX Pros and Cons
Lecture 21: What if the frequency is set to 365?
Lecture 22: SARIMAX challenge introduction
Lecture 23: R – SARIMAX challenge solutions
Lecture 24: Diogo's tip: choosing regressors
Chapter 7: Facebook Prophet
Lecture 1: Game Plan for Facebook Prophet
Lecture 2: Structural Time Series
Lecture 3: Facebook Prophet
Lecture 4: R – Preparing Script
Lecture 5: Holidays
Lecture 6: R – Holidays part 1
Lecture 7: R – Holidays part 2
Lecture 8: R – Prophet Holiday Dataframe Merge
Lecture 9: R – Training and Test Set
Lecture 10: Facebook Prophet parameters
Lecture 11: R – Prophet Model
Lecture 12: R – Regressor Coefficients
Lecture 13: R – Forecasting
Lecture 14: R – Impact of the events
Lecture 15: R – Visualization
Lecture 16: R – Prophet accuracy and export
Lecture 17: Facebook Prophet Pros and Cons
Lecture 18: Facebook Prophet Challenge
Lecture 19: R – Facebook Prophet solutions part 1
Lecture 20: R – Facebook Prophet solutions part 2
Chapter 8: Facebook Prophet – Parameter Tuning
Lecture 1: Game Plan for Facebook Prophet – Parameter Tuning
Lecture 2: Parameter Tuning
Lecture 3: Cross-validation
Lecture 4: R – Preparing Script
Instructors
-
Diogo Alves de Resende
Analytics and Data Science expert
Rating Distribution
- 1 stars: 4 votes
- 2 stars: 4 votes
- 3 stars: 34 votes
- 4 stars: 103 votes
- 5 stars: 172 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
You may also like
- Top 10 Video Editing Courses to Learn in November 2024
- Top 10 Music Production Courses to Learn in November 2024
- Top 10 Animation Courses to Learn in November 2024
- Top 10 Digital Illustration Courses to Learn in November 2024
- Top 10 Renewable Energy Courses to Learn in November 2024
- Top 10 Sustainable Living Courses to Learn in November 2024
- Top 10 Ethical AI Courses to Learn in November 2024
- Top 10 Cybersecurity Fundamentals Courses to Learn in November 2024
- Top 10 Smart Home Technology Courses to Learn in November 2024
- Top 10 Holistic Health Courses to Learn in November 2024
- Top 10 Nutrition And Diet Planning Courses to Learn in November 2024
- Top 10 Yoga Instruction Courses to Learn in November 2024
- Top 10 Stress Management Courses to Learn in November 2024
- Top 10 Mindfulness Meditation Courses to Learn in November 2024
- Top 10 Life Coaching Courses to Learn in November 2024
- Top 10 Career Development Courses to Learn in November 2024
- Top 10 Relationship Building Courses to Learn in November 2024
- Top 10 Parenting Skills Courses to Learn in November 2024
- Top 10 Home Improvement Courses to Learn in November 2024
- Top 10 Gardening Courses to Learn in November 2024