Introduction to Time Series Analysis and Forecasting in R
4.3 (1,644 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
8,039 students enrolled

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
4.3 (1,644 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
8,037 students enrolled
Last updated 3/2019
English
English [Auto-generated]
Current price: $72.99 Original price: $104.99 Discount: 30% off
5 hours left at this price!
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
  • Certificate of Completion
Training 5 or more people?

Get your team access to 4,000+ top Udemy courses anytime, anywhere.

Try Udemy for Business
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
Course content
Expand all 74 lectures 08:32:54
+ Introduction
10 lectures 01:22:09
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
+ Working With Dates And Time In R
8 lectures 39:49
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
+ Time Series Data Pre-Processing and Visualization
8 lectures 01:10:34
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
+ Statistical Background For Time Series Analysis And Forecasting
10 lectures 01:15:36
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
+ Time Series Analysis And Forecasting
8 lectures 01:03:22
Selecting a Suitable Model - Quantitative Forecasting Models
08:18
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
+ ARIMA Models
10 lectures 01:32:40
What is Coming Up Next? ARIMA Models in Time Series Analysis
01:51
Introduction to ARIMA Models
10:21
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
+ Multivariate Time Series Analysis
11 lectures 58:02
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
+ Neural Networks in Time Series Analysis
9 lectures 30:38
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!