Introduction to Time Series Analysis and Forecasting in R

Introduction to Time Series Analysis and Forecasting in R

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

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 datamodel this datafinally 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?

Overview

Section 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

Section 2: Working With Dates And Time In R

Lecture 11 Welcome to this Section – What Is this Section About?

Lecture 12 Working with Different Date and Time Classes: POSIXt, Date and Chron

Lecture 13 Format Conversion from String to Date / Time – Function strptime

Lecture 14 The Lubridate Package

Lecture 15 Exercise: Using Lubridate on a Data Frame

Lecture 16 Date and Time Calculations with Lubridate

Lecture 17 Lubridate: Data Handling Exercise

Lecture 18 Section Script TD

Section 3: Time Series Data Pre-Processing and Visualization

Lecture 19 Creating Time Series

Lecture 20 Exercise – Time Series Formatting

Lecture 21 Time Series Charts and Graphs

Lecture 22 Exercise: Seasonplot

Lecture 23 Importing Time Series Data From Excel or Other Sources

Lecture 24 Working with Irregular Time Series

Lecture 25 Working with Missing Data and Outliers

Lecture 26 Section Script TSPP

Section 4: Statistical Background For Time Series Analysis And Forecasting

Lecture 27 Time Series Vectors and Lags

Lecture 28 Time Series Characteristics

Lecture 29 Basic Forecasting Models

Lecture 30 Model Comparison and Accuracy

Lecture 31 The Importance of Residuals in Time Series Analysis

Lecture 32 Stationarity

Lecture 33 Autocorrelation

Lecture 34 Functions acf() and pacf()

Lecture 35 Exercise: Forecast Comparison

Lecture 36 Section Script STAT

Section 5: Time Series Analysis And Forecasting

Lecture 37 Selecting a Suitable Model – Quantitative Forecasting Models

Lecture 38 Seasonal Decomposition Intro

Lecture 39 Decomposition Demo

Lecture 40 Exercise: Decomposition

Lecture 41 Simple Moving Average

Lecture 42 Exponential Smoothing with ETS

Lecture 43 Judgmental Forecasts – Qualitative Forecasting Methods

Lecture 44 Section Script TSA

Section 6: ARIMA Models

Lecture 45 What is Coming Up Next? ARIMA Models in Time Series Analysis

Lecture 46 Introduction to ARIMA Models

Lecture 47 Automated ARIMA Model Selection with auto.arima

Lecture 48 ARIMA Model Calculations

Lecture 49 Simulating Time Series Based on ARIMA

Lecture 50 Manual ARIMA Parameter Selection

Lecture 51 How to Indentify ARIMA Model Parameters

Lecture 52 ARIMA Forecasts

Lecture 53 ARIMA with Explanatory Variables – Adding a Second Variable to the Model

Lecture 54 Section Script ARIMA

Section 7: Multivariate Time Series Analysis

Lecture 55 What is Coming Up Next? Multivariate Time Series Analysis in R

Lecture 56 Understanding Multivariate Time Series and Their Structure

Lecture 57 Multivariate Time Series Objects and Project Dataset

Lecture 58 Main R Packages for Multivariate Time Series Analysis

Lecture 59 Stationarity in Multivariate Time Series

Lecture 60 Vector Autoregressive Model Theory

Lecture 61 Implementing VAR Models in R

Lecture 62 Test for Residual Correlation and Model Diagnostics

Lecture 63 The Granger Test for Causality

Lecture 64 Forecasting a VAR Model

Lecture 65 Section Script

Section 8: Neural Networks in Time Series Analysis

Lecture 66 What is Coming Up Next? Time Series Analysis Using Neural Networks

Lecture 67 Intro to Neural Networks for TSA

Lecture 68 Getting Familiar with the Dataset

Lecture 69 The Time Series Task View for Neural Nets – What is Available?

Lecture 70 Implementation of Neural Networks in R – Underlying Functions

Lecture 71 Practical Implementation of an Autoregressive Neural Net

Lecture 72 Implementing an External Regressor – Multivariate Neural Net

Lecture 73 Section Script

Lecture 74 Further Resources and Where to Go Next

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!

Course Information:

Udemy | English | 8h 33m | 4.01 GB
Created by: R-Tutorials Training

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