Intro to Time Series Analysis in R

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In this Guided Project, you will:

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.

Clock2 hours
BeginnerBeginner
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

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

Time Series ForecastingTime SeriesTime Series ModelsBox Jenkins MethodStatistical Hypothesis Testing

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:

  1. Time Series Data Overview

  2. Why Time Series?

  3. Key Concepts: Autocorrelation / Autocovariance

  4. Key Concepts: Stationarity

  5. Checking for Stationarity

  6. Transforming for Stationarity

  7. Basic Model Types: AR, MA, ARMA, ARIMA, Decomposition

  8. 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

Instructor

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Frequently Asked Questions

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