Udemy
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
Development
Web Development Data Science Mobile Development Programming Languages Game Development Database Design & Development Software Testing Software Engineering Development Tools No-Code Development
Business
Entrepreneurship Communications Management Sales Business Strategy Operations Project Management Business Law Business Analytics & Intelligence Human Resources Industry E-Commerce Media Real Estate Other Business
Finance & Accounting
Accounting & Bookkeeping Compliance Cryptocurrency & Blockchain Economics Finance Finance Cert & Exam Prep Financial Modeling & Analysis Investing & Trading Money Management Tools Taxes Other Finance & Accounting
IT & Software
IT Certification Network & Security Hardware Operating Systems Other IT & Software
Office Productivity
Microsoft Apple Google SAP Oracle Other Office Productivity
Personal Development
Personal Transformation Personal Productivity Leadership Career Development Parenting & Relationships Happiness Esoteric Practices Religion & Spirituality Personal Brand Building Creativity Influence Self Esteem & Confidence Stress Management Memory & Study Skills Motivation Other Personal Development
Design
Web Design Graphic Design & Illustration Design Tools User Experience Design Game Design Design Thinking 3D & Animation Fashion Design Architectural Design Interior Design Other Design
Marketing
Digital Marketing Search Engine Optimization Social Media Marketing Branding Marketing Fundamentals Marketing Analytics & Automation Public Relations Advertising Video & Mobile Marketing Content Marketing Growth Hacking Affiliate Marketing Product Marketing Other Marketing
Lifestyle
Arts & Crafts Beauty & Makeup Esoteric Practices Food & Beverage Gaming Home Improvement Pet Care & Training Travel Other Lifestyle
Photography & Video
Digital Photography Photography Portrait Photography Photography Tools Commercial Photography Video Design Other Photography & Video
Health & Fitness
Fitness General Health Sports Nutrition Yoga Mental Health Dieting Self Defense Safety & First Aid Dance Meditation Other Health & Fitness
Music
Instruments Music Production Music Fundamentals Vocal Music Techniques Music Software Other Music
Teaching & Academics
Engineering Humanities Math Science Online Education Social Science Language Teacher Training Test Prep Other Teaching & Academics
AWS Certification Microsoft Certification AWS Certified Solutions Architect - Associate AWS Certified Cloud Practitioner CompTIA A+ Cisco CCNA Amazon AWS CompTIA Security+ AWS Certified Developer - Associate
Graphic Design Photoshop Adobe Illustrator Drawing Digital Painting InDesign Character Design Canva Figure Drawing
Life Coach Training Neuro-Linguistic Programming Personal Development Mindfulness Personal Transformation Meditation Life Purpose Coaching Emotional Intelligence
Web Development JavaScript React CSS Angular PHP WordPress Node.Js Python
Google Flutter Android Development iOS Development Swift React Native Dart Programming Language Mobile Development Kotlin SwiftUI
Digital Marketing Google Ads (Adwords) Social Media Marketing Google Ads (AdWords) Certification Marketing Strategy Internet Marketing YouTube Marketing Email Marketing Retargeting
SQL Microsoft Power BI Tableau Business Analysis Business Intelligence MySQL Data Analysis Data Modeling Data Cleaning
Business Fundamentals Entrepreneurship Fundamentals Business Strategy Online Business Business Plan Startup Freelancing Blogging Home Business
Unity Game Development Fundamentals Unreal Engine C# 3D Game Development C++ 2D Game Development Unreal Engine Blueprints Blender
30-Day Money-Back Guarantee

This course includes:

  • 8.5 hours on-demand video
  • 9 articles
  • 5 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
Development Data Science R

Introduction to Time Series Analysis and Forecasting in R

Work with time series and all sorts of time related data in R - Forecasting, Time Series Analysis, Predictive Analytics
Bestseller
Rating: 4.3 out of 54.3 (1,988 ratings)
9,764 students
Created by R-Tutorials Training
Last updated 3/2019
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • use R to perform calculations with time and date based data
  • create models for time series data
  • use models for forecasting
  • identify which models are suitable for a given dataset
  • visualize time series data
  • transform standard data into time series format
  • clean and pre-process time series
  • create ARIMA and exponential smoothing models
  • know how to interpret given models
  • identify the best time series libraries for a given problem
  • compare the accuracy of different models
Curated for the Udemy for Business collection

Course content

8 sections • 74 lectures • 8h 32m total length

  • Preview04:19
  • Preview04:57
  • Basics of Time Series Analysis and Forecasting
    14:06
  • Method Selection in Forecasting
    05:56
  • Forecasting: Step by Step Guide
    10:08
  • Time Series Analysis and Forecasting Use Case: IT Store Staff Allocation
    24:22
  • Script for the Example
    00:17
  • Package Overview and the R Time Series Task View
    10:26
  • Datasets To Be Used
    07:22
  • Course Links
    00:16
  • Time Series Analysis Intro
    4 questions

  • Welcome to this Section - What Is this Section About?
    02:42
  • Working with Different Date and Time Classes: POSIXt, Date and Chron
    08:43
  • Format Conversion from String to Date / Time - Function strptime
    05:30
  • The Lubridate Package
    06:32
  • Exercise: Using Lubridate on a Data Frame
    05:50
  • Date and Time Calculations with Lubridate
    04:53
  • Lubridate: Data Handling Exercise
    03:43
  • Section Script TD
    01:56

  • Creating Time Series
    09:05
  • Exercise - Time Series Formatting
    03:58
  • Time Series Charts and Graphs
    10:37
  • Exercise: Seasonplot
    04:22
  • Importing Time Series Data From Excel or Other Sources
    05:55
  • Working with Irregular Time Series
    22:15
  • Working with Missing Data and Outliers
    12:25
  • Section Script TSPP
    01:57
  • Time Series Data Preparation
    5 questions

  • Time Series Vectors and Lags
    07:33
  • Time Series Characteristics
    05:29
  • Basic Forecasting Models
    08:33
  • Model Comparison and Accuracy
    08:51
  • The Importance of Residuals in Time Series Analysis
    06:15
  • Stationarity
    07:30
  • Autocorrelation
    07:30
  • Functions acf() and pacf()
    06:23
  • Exercise: Forecast Comparison
    16:22
  • Section Script STAT
    01:10
  • Statistical Background
    5 questions

  • Selecting a Suitable Model - Quantitative Forecasting Models
    08:18
  • Preview10:20
  • Decomposition Demo
    04:24
  • Exercise: Decomposition
    09:58
  • Simple Moving Average
    04:25
  • Exponential Smoothing with ETS
    13:22
  • Judgmental Forecasts - Qualitative Forecasting Methods
    11:50
  • Section Script TSA
    00:45

  • What is Coming Up Next? ARIMA Models in Time Series Analysis
    01:51
  • Introduction to ARIMA Models
    10:21
  • Automated ARIMA Model Selection with auto.arima
    Preview15:07
  • ARIMA Model Calculations
    16:16
  • Simulating Time Series Based on ARIMA
    10:01
  • Manual ARIMA Parameter Selection
    13:40
  • How to Indentify ARIMA Model Parameters
    06:17
  • ARIMA Forecasts
    05:04
  • ARIMA with Explanatory Variables - Adding a Second Variable to the Model
    12:54
  • Section Script ARIMA
    01:09

  • What is Coming Up Next? Multivariate Time Series Analysis in R
    03:06
  • Understanding Multivariate Time Series and Their Structure
    10:37
  • Multivariate Time Series Objects and Project Dataset
    04:37
  • Main R Packages for Multivariate Time Series Analysis
    02:16
  • Stationarity in Multivariate Time Series
    04:23
  • Vector Autoregressive Model Theory
    04:45
  • Implementing VAR Models in R
    08:45
  • Test for Residual Correlation and Model Diagnostics
    02:31
  • The Granger Test for Causality
    04:34
  • Forecasting a VAR Model
    11:46
  • Section Script
    00:42

  • What is Coming Up Next? Time Series Analysis Using Neural Networks
    01:18
  • Intro to Neural Networks for TSA
    06:02
  • Getting Familiar with the Dataset
    03:34
  • The Time Series Task View for Neural Nets - What is Available?
    02:14
  • Implementation of Neural Networks in R - Underlying Functions
    03:06
  • Practical Implementation of an Autoregressive Neural Net
    06:31
  • Implementing an External Regressor - Multivariate Neural Net
    03:49
  • Section Script
    00:10
  • Further Resources and Where to Go Next
    03:54

Requirements

  • computer with R and RStudio ready to use
  • interest in statistics and programming
  • time to solve the exercises
  • basic knowledge of R (course R Base)
  • NO advanced statistics or maths knowledge required

Description

Understand the Now – Predict the Future!

Time series analysis and forecasting is one of the key fields in statistical programming. It allows you to

  • see patterns in time series data
  • model this data
  • finally make forecasts based on those models

Due to modern technology the amount of available data grows substantially from day to day. Successful companies know that. They also know that decisions based on data gained in the past, and modeled for the future, can make a huge difference. Proper understanding and training in time series analysis and forecasting will give you the power to understand and create those models. This can make you an invaluable asset for your company/institution and will boost your career!

  • What will you learn in this course and how is it structured?

You will learn about different ways in how you can handle date and time data in R. Things like time zones, leap years or different formats make calculations with dates and time especially tricky for the programmer. You will learn about POSIXt classes in R Base, the chron package and especially the lubridate package.

You will learn how to visualize, clean and prepare your data. Data preparation takes a huge part of your time as an analyst. Knowing the best functions for outlier detection, missing value imputation and visualization can safe your day.

After that you will learn about statistical methods used for time series. You will hear about autocorrelation, stationarity and unit root tests.

Then you will see how different models work, how they are set up in R and how you can use them for forecasting and predictive analytics. Models taught are: ARIMA, exponential smoothing, seasonal decomposition and simple models acting as benchmarks. Of course all of this is accompanied with plenty of exercises.

  • Where are those methods applied?

In nearly any quantitatively working field you will see those methods applied. Especially econometrics and finance love time series analysis. For example stock data has a time component which makes this sort of data a prime target for forecasting techniques. But of course also in academia, medicine, business or marketing techniques taught in this course are applied.

  • Is it hard to understand and learn those methods?

Unfortunately learning material on Time Series Analysis Programming in R is quite technical and needs tons of prior knowledge to be understood.

With this course it is the goal to make understanding modeling and forecasting as intuitive and simple as possible for you.

While you need some knowledge in statistics and statistical programming, the course is meant for people without a major in a quantitative field like math or statistics. Basically anybody dealing with time data on a regular basis can benefit from this course.

  • How do I prepare best to benefit from this course?

It depends on your prior knowledge. But as a rule of thumb you should know how to handle standard tasks in R (course R Basics).

What R you waiting for?

Who this course is for:

  • this course is for people working with time series data
  • this course is for people interested in R
  • this course is for people with some beginner knowledge in both R programming and statistics
  • this course is for people working in various fields like (and not limited to): academia, marketing, business, econometrics, finance, medicine, engineering and science
  • generally if you have time series data on your table and you do not know what to do with it, take this course!

Instructor

R-Tutorials Training
Data Science Education
R-Tutorials Training
  • 4.4 Instructor Rating
  • 26,631 Reviews
  • 217,358 Students
  • 23 Courses

  R-Tutorials is your provider of choice when it comes to analytics training courses! Try it out – our 100,000+ students love it. 

        We focus on Data Science tutorials. Offering several R courses for every skill level, we are among Udemy's top R training provider. On top of that courses on Tableau, Excel and a Data Science career guide are available.

        All of our courses contain exercises to give you the opportunity to try out the material on your own. You will also get downloadable script pdfs to recap the lessons. 

        The courses are taught by our main instructor Martin – trained biostatistician and enthusiastic data scientist / R user. 

        Should you have any questions, you are invited to check out our website, you can open a discussion in the course or you can simply drop us a pm. 

        We are here to help you boost your career with analytics training – Just learn and enjoy. 

  • Udemy for Business
  • Teach on Udemy
  • Get the app
  • About us
  • Contact us
  • Careers
  • Blog
  • Help and Support
  • Affiliate
  • Terms
  • Privacy policy
  • Cookie settings
  • Sitemap
  • Featured courses
Udemy
© 2021 Udemy, Inc.