Let us help you find the training program you are looking for.

If you can't find what you are looking for, contact us, we'll help you find it. We have over 800 training programs to choose from.

Time Series Analysis with R

  • Course Code: Data Science - Time Series Analysis with R
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 3 Days Audience: This course is geared for those who wants to Build efficient forecasting models using traditional time series models and machine learning algorithms.

Course Snapshot 

  • Course: Time Series Analysis with R 
  • Duration: 3 days 
  • Skill-level: Foundation-level Time Series Analysis with R skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Build efficient forecasting models using traditional time series models and machine learning algorithms. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Time series analysis is the art of extracting meaningful insights from, and revealing patterns in, time series data using statistical and data visualization approaches. These insights and patterns can then be utilized to explore past events and forecast future values in the series. This course explores the basics of time series analysis with R and lays the foundations you need to build forecasting models. You will learn how to preprocess raw time series data and clean and manipulate data with packages such as stats, lubridate, xts, and zoo. You will analyze data and extract meaningful information from it using both descriptive statistics and rich data visualization tools in R such as the TSstudio, plotly, and ggplot2 packages. The later section of the course delves into traditional forecasting models such as time series linear regression, exponential smoothing (Holt, Holt-Winter, and more) and Auto-Regressive Integrated Moving Average (ARIMA) models with the stats and forecast packages. You’ll also cover advanced time series regression models with machine learning algorithms such as Random Forest and Gradient Boosting Machine using the h2o package. By the end of this course, you will have the skills needed to explore your data, identify patterns, and build a forecasting model using various traditional and machine learning methods. 

Working in a hands-on learning environment, led by Time Series Analysis with R expert instructor, students will learn about and explore: 

  • Perform time series analysis and forecasting using R packages such as Forecast and h2o 
  • Develop models and find patterns to create visualizations using the TSstudio and plotly packages 
  • Master statistics and implement time-series methods using examples mentioned 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Visualize time series data and derive better insights 
  • Explore auto-correlation and master statistical techniques 
  • Use time series analysis tools from the stats, TSstudio, and forecast packages 
  • Explore and identify seasonal and correlation patterns 
  • Work with different time series formats in R 
  • Explore time series models such as ARIMA, Holt-Winters, and more 
  • Evaluate high-performance forecasting solutions 

Audience & Pre-Requisites 

This course is for readers want to Build efficient forecasting models using traditional time series models and machine learning algorithms.. 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills. 
  • Good foundational mathematics or logic skills 
  • For readers with existing programming skills. 

Course Agenda / Topics 

  1. Introduction to Time Series Analysis and R 
  • Introduction to Time Series Analysis and R 
  • Technical requirements 
  • Time series data 
  • Historical background of time series analysis 
  • Time series analysis 
  • Getting started with R 
  • A brief introduction to R 
  • Working and manipulating data 
  1. Working with Date and Time Objects 
  • Working with Date and Time Objects 
  • Technical requirements 
  • The date and time formats 
  • Date and time objects in R 
  • Creating a date or time index 
  • Manipulation of date and time with the lubridate package 
  1. The Time Series Object 
  • The Time Series Object 
  • Technical requirement 
  • The Natural Gas Consumption dataset 
  • The attributes of the ts class 
  • Data manipulation of ts objects 
  • Visualizing ts and mts objects 
  1. Working with zoo and xts Objects 
  • Working with zoo and xts Objects 
  • Technical requirement 
  • The zoo class 
  • The xts class 
  • Manipulating the zoo and xts objects 
  • Plotting zoo and xts objects 
  • xts, zoo, or ts – which one to use? 
  1. Decomposition of Time Series Data 
  • Decomposition of Time Series Data 
  • Technical requirement 
  • The moving average function 
  • The time series components 
  • The additive versus the multiplicative model 
  • The decomposition of time series 
  • Seasonal adjustment 
  1. Seasonality Analysis 
  • Seasonality Analysis 
  • Technical requirement 
  • Seasonality types 
  • Seasonal analysis with descriptive statistics 
  • Structural tools for seasonal analysis 
  1. Correlation Analysis 
  • Correlation Analysis 
  • Technical requirement 
  • Correlation between two variables 
  • Lags analysis 
  • The autocorrelation function 
  • The partial autocorrelation function 
  • Lag plots 
  • Causality analysis 
  1. Forecasting Strategies 
  • Forecasting Strategies 
  • Technical requirement 
  • The forecasting workflow 
  • Training approaches 
  • Finalizing the forecast 
  • Handling forecast uncertainty 
  1. Forecasting with Linear Regression 
  • Forecasting with Linear Regression 
  • Technical requirement 
  • The linear regression 
  • Forecasting with linear regression 
  • Forecasting a series with multiseasonality components – a case study 
  1. Forecasting with Exponential Smoothing Models 
  • Forecasting with Exponential Smoothing Models 
  • Technical requirement 
  • Forecasting with moving average models 
  • Forecasting with exponential smoothing 
  1. Forecasting with ARIMA Models 
  • Forecasting with ARIMA Models 
  • Technical requirement 
  • The stationary process 
  • The AR process 
  • The moving average process 
  • The ARMA model 
  • Forecasting AR, MA, and ARMA models 
  • The ARIMA model 
  • The seasonal ARIMA model 
  • The auto.arima function 
  • Linear regression with ARIMA errors 
  1. Forecasting with Machine Learning Models 
  • Forecasting with Machine Learning Models 
  • Technical requirement 
  • Why and when should we use machine learning? 
  • Why h2o? 
  • Forecasting monthly vehicle sales in the US – a case study 

Student Materials: Each student will receive a Student Guide with course notes, code samples, software tutorials, diagrams and related reference materials and links (as applicable). Our courses also include step by step hands-on lab instructions and and solutions, clearly illustrated for users to complete hands-on work in class, and to revisit to review or refresh skills at any time. Students will also receive the project files (or code, if applicable) and solutions required for the hands-on work. 

View All Courses

    Course Inquiry

    Fill in the details below and we will get back to you as quickly as we can.

    Interested in any of these related courses?