Intro to Time Series Analysis in R
Fit various types of time series models to real world data and use them to forecast the future.
Understand how to assess model fit in time series data.
Know the reasons why time series models and methodology are an important toolkit for any data scientist.
In this 2 hour long project-based course, you will learn the basics of time series analysis in R. By the end of this project, you will understand the essential theory for time series analysis and have built each of the major model types (Autoregressive, Moving Average, ARMA, ARIMA, and decomposition) on a real world data set to forecast the future. We will go over the essential packages and functions in R as well to make time series analysis easy.
Skills you will develop
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Time Series Data Overview
Why Time Series?
Key Concepts: Autocorrelation / Autocovariance
Key Concepts: Stationarity
Checking for Stationarity
Transforming for Stationarity
Basic Model Types: AR, MA, ARMA, ARIMA, Decomposition
And More!
How Guided Projects work
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
Reviews
TOP REVIEWS FROM INTRO TO TIME SERIES ANALYSIS IN R
Great summary of time series modelling. The external tool is a bit annoying but the course is great. The student start by downloading all materials and then use RStudio Cloud.
Exceptional! A background in basic statistics (hypothesis test and regression) . as well as a basic understanding of R is a nice-to-have. But not mandatory.
I actually couldn't load the work space to use the Jupyter notebook (could have to do with my outdated iOS?) but I just wrote the code in R and it was fine
Not to bad for being a short guided course. Concise and a good overview. After this you may very well do a serious course if you want to get anywhere
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