Introduction to Time Series Analysis and Forecasting in R

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