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Course Skill Level:

Foundational to Intermediate

Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Professionals with basic IT & programming skills who want to build forecasting models

Who should attend & recommended skills

  • Those looking to build efficient forecasting models using traditional time series models and machine learning algorithms.
  • Skill-level: Foundation-level Time Series Analysis with R is for Intermediate skilled team members. This is not a basic class.
  • IT skills: Basic to Intermediate (1-5 years’ experience)
  • Programming skills: Basic (1-2 years’ experience)

About this course

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.

Skills acquired & topics covered

  • Performing time series analysis and forecasting using R packages such as Forecast and h2o
  • Developing models and find patterns to create visualizations using the TSstudio and plotly packages
  • Mastering statistics and implement time-series methods using examples mentioned
  • Visualizing time series data and derive better insights
  • Auto-correlation and master statistical techniques
  • Using time series analysis tools from the stats, TSstudio, and forecast packages
  • Exploring and identifying seasonal and correlation patterns
  • Working with different time series formats in R
  • Time series models such as ARIMA, Holt-Winters, and more
  • Evaluating high-performance forecasting solutions

Course breakdown / modules

  • 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

  • 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

  • Technical requirement
  • The Natural Gas Consumption dataset
  • The attributes of the ts class
  • Data manipulation of ts objects
  • Visualizing ts and mts 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?

  • Technical requirement
  • The moving average function
  • The time series components
  • The additive versus the multiplicative model
  • The decomposition of time series
  • Seasonal adjustment

  • Technical requirement
  • Seasonality types
  • Seasonal analysis with descriptive statistics
  • Structural tools for seasonal analysis

  • Technical requirement
  • Correlation between two variables
  • Lags analysis
  • The autocorrelation function
  • The partial autocorrelation function
  • Lag plots
  • Causality analysis

  • Technical requirement
  • The forecasting workflow
  • Training approaches
  • Finalizing the forecast
  • Handling forecast uncertainty

  • Technical requirement
  • The linear regression
  • Forecasting with linear regression
  • Forecasting a series with multiseasonality components – a case study

  • Technical requirement
  • Forecasting with moving average models
  • Forecasting with exponential smoothing

  • 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

  • Technical requirement
  • Why and when should we use machine learning?
  • Why h2o?
  • Forecasting monthly vehicle sales in the US – a case study