Introduction to Time Series Analysis and Forecasting in R

Introduction to Time Series Analysis and Forecasting in R, available at $84.99, has an average rating of 4.38, with 75 lectures, 3 quizzes, based on 2724 reviews, and has 14243 subscribers.

You will learn about 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 This course is ideal for individuals who are this course is for people working with time series data or this course is for people interested in R or this course is for people with some beginner knowledge in both R programming and statistics or this course is for people working in various fields like (and not limited to): academia, marketing, business, econometrics, finance, medicine, engineering and science or generally if you have time series data on your table and you do not know what to do with it, take this course! It is particularly useful for this course is for people working with time series data or this course is for people interested in R or this course is for people with some beginner knowledge in both R programming and statistics or this course is for people working in various fields like (and not limited to): academia, marketing, business, econometrics, finance, medicine, engineering and science or generally if you have time series data on your table and you do not know what to do with it, take this course!.

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Summary

Title: Introduction to Time Series Analysis and Forecasting in R

Price: $84.99

Average Rating: 4.38

Number of Lectures: 75

Number of Quizzes: 3

Number of Published Lectures: 74

Number of Published Quizzes: 3

Number of Curriculum Items: 78

Number of Published Curriculum Objects: 77

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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!

Target Audiences

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!

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?

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome to the Course Introduction to Time Series Analysis and Forecasting in R

Lecture 2: Managing Expectations

Lecture 3: Basics of Time Series Analysis and Forecasting

Lecture 4: Method Selection in Forecasting

Lecture 5: Forecasting: Step by Step Guide

Lecture 6: Time Series Analysis and Forecasting Use Case: IT Store Staff Allocation

Lecture 7: Script for the Example

Lecture 8: Package Overview and the R Time Series Task View

Lecture 9: Datasets To Be Used

Lecture 10: Course Links

Chapter 2: Working With Dates And Time In R

Lecture 1: Welcome to this Section – What Is this Section About?

Lecture 2: Working with Different Date and Time Classes: POSIXt, Date and Chron

Lecture 3: Format Conversion from String to Date / Time – Function strptime

Lecture 4: The Lubridate Package

Lecture 5: Exercise: Using Lubridate on a Data Frame

Lecture 6: Date and Time Calculations with Lubridate

Lecture 7: Lubridate: Data Handling Exercise

Lecture 8: Section Script TD

Chapter 3: Time Series Data Pre-Processing and Visualization

Lecture 1: Creating Time Series

Lecture 2: Exercise – Time Series Formatting

Lecture 3: Time Series Charts and Graphs

Lecture 4: Exercise: Seasonplot

Lecture 5: Importing Time Series Data From Excel or Other Sources

Lecture 6: Working with Irregular Time Series

Lecture 7: Working with Missing Data and Outliers

Lecture 8: Section Script TSPP

Chapter 4: Statistical Background For Time Series Analysis And Forecasting

Lecture 1: Time Series Vectors and Lags

Lecture 2: Time Series Characteristics

Lecture 3: Basic Forecasting Models

Lecture 4: Model Comparison and Accuracy

Lecture 5: The Importance of Residuals in Time Series Analysis

Lecture 6: Stationarity

Lecture 7: Autocorrelation

Lecture 8: Functions acf() and pacf()

Lecture 9: Exercise: Forecast Comparison

Lecture 10: Section Script STAT

Chapter 5: Time Series Analysis And Forecasting

Lecture 1: Selecting a Suitable Model – Quantitative Forecasting Models

Lecture 2: Seasonal Decomposition Intro

Lecture 3: Decomposition Demo

Lecture 4: Exercise: Decomposition

Lecture 5: Simple Moving Average

Lecture 6: Exponential Smoothing with ETS

Lecture 7: Judgmental Forecasts – Qualitative Forecasting Methods

Lecture 8: Section Script TSA

Chapter 6: ARIMA Models

Lecture 1: What is Coming Up Next? ARIMA Models in Time Series Analysis

Lecture 2: Introduction to ARIMA Models

Lecture 3: Automated ARIMA Model Selection with auto.arima

Lecture 4: ARIMA Model Calculations

Lecture 5: Simulating Time Series Based on ARIMA

Lecture 6: Manual ARIMA Parameter Selection

Lecture 7: How to Indentify ARIMA Model Parameters

Lecture 8: ARIMA Forecasts

Lecture 9: ARIMA with Explanatory Variables – Adding a Second Variable to the Model

Lecture 10: Section Script ARIMA

Chapter 7: Multivariate Time Series Analysis

Lecture 1: What is Coming Up Next? Multivariate Time Series Analysis in R

Lecture 2: Understanding Multivariate Time Series and Their Structure

Lecture 3: Multivariate Time Series Objects and Project Dataset

Lecture 4: Main R Packages for Multivariate Time Series Analysis

Lecture 5: Stationarity in Multivariate Time Series

Lecture 6: Vector Autoregressive Model Theory

Lecture 7: Implementing VAR Models in R

Lecture 8: Test for Residual Correlation and Model Diagnostics

Lecture 9: The Granger Test for Causality

Lecture 10: Forecasting a VAR Model

Lecture 11: Section Script

Chapter 8: Neural Networks in Time Series Analysis

Lecture 1: What is Coming Up Next? Time Series Analysis Using Neural Networks

Lecture 2: Intro to Neural Networks for TSA

Lecture 3: Getting Familiar with the Dataset

Lecture 4: The Time Series Task View for Neural Nets – What is Available?

Lecture 5: Implementation of Neural Networks in R – Underlying Functions

Lecture 6: Practical Implementation of an Autoregressive Neural Net

Lecture 7: Implementing an External Regressor – Multivariate Neural Net

Lecture 8: Section Script

Lecture 9: Further Resources and Where to Go Next

Instructors

R-Tutorials Training
Data Science Education

Rating Distribution

1 stars: 51 votes

2 stars: 86 votes

3 stars: 374 votes

4 stars: 1030 votes

5 stars: 1184 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!