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Time Series Analysis Using R
Course 1269
- Duration: 1 day
 - Language: English
 - Level: Foundation
 
A time series is simply a series of data points arranged chronologically.
Time series appear in many contexts.
- Finance (e.g. stock prices)
 - Marketing (e.g. new customers, sales)
 - Health (e.g. infections, deaths)
 - Science (e.g earthquakes)
 - ...and many, many more
 
Train your whole team by bringing this course to your facility.
Team training is available online and in-person.
Time Series Analysis Course Information
In this course, you will:
- Work with the R statistical modeling language to analyze data.
 - Import internal and external data sources into R.
 - Work with time-series data.
 - Visualize time-series data.
 - Perform automated forecasting using Prophet.
 - Consider the potential impact of machine learning on time series analysis.
 - Leverage continued support with after-course one-on-one instructor coaching and computing sandbox.
 
Prerequisites
None.
Time Series Analysis Instructor-Led Course Outline
- Basics of writing R scripts
 - Viewing data sets in R
 
- Querying and Saving Data in R
 - Exploratory Data Analysis
 - Visualizing Data
 - Transforming Data
 
- Time Series in R
 - Creating Time Series
 - Querying and Data Manipulation
 - Combining Time Series
 - Rate of Change
 - Applying Functions to Intervals
 
- Plotting Basics
 - Univariate Time Series
 - Multivariate Time Series
 
- Generate Forecasts using Prophet
 - Forecast COVID-19 Cases
 
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Time Series Analysis FAQs
No. You will be taught basic R skills as part of the course.
This is an introductory course. If you have a favorite time series technique, then you are possibly already beyond this course. The focus is very much on the basics. We make extensive use of the popular `xts` package and introduce Meta's (Facebook's) Prophet library.
The focus of the course is on time series analysis. R is merely used as a tool to that end. So, yes---the concepts will translate to your preferred environment.
Yes. There are various opportunities to apply the ideas presented to real-world time series data.