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Time Series Analysis in R

Learn the core techniques necessary to extract meaningful insights from time series data.

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4 Hours16 Videos58 Exercises49,545 Learners4600 XPQuantitative Analyst TrackTime Series Track

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Course Description

Many phenomena in our day-to-day lives, such as the movement of stock prices, are measured in intervals over a period of time. Time series analysis methods are extremely useful for analyzing these special data types. In this course, you will be introduced to some core time series analysis concepts and techniques.

  1. 1

    Exploratory time series data analysis


    This chapter will give you insights on how to organize and visualize time series data in R. You will learn several simplifying assumptions that are widely used in time series analysis, and common characteristics of financial time series.

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    Welcome to the course!
    50 xp
    Exploring raw time series
    100 xp
    Basic time series plots
    100 xp
    What does the time index tell us?
    100 xp
    Sampling frequency
    50 xp
    Identifying the sampling frequency
    100 xp
    When is the sampling frequency exact?
    50 xp
    Missing values
    100 xp
    Basic time series objects
    50 xp
    Creating a time series object with ts()
    100 xp
    Testing whether an object is a time series
    100 xp
    Plotting a time series object
    100 xp
  2. 3

    Correlation analysis and the autocorrelation function

    In this chapter, you will review the correlation coefficient, use it to compare two time series, and also apply it to compare a time series with its past, as an autocorrelation. You will discover the autocorrelation function (ACF) and practice estimating and visualizing autocorrelations for time series data.

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In the following tracks

Quantitative AnalystTime Series


loreLore DirickmattisaacsMatt Isaacs


Intermediate R
David S. Matteson Headshot

David S. Matteson

Associate Professor at Cornell University

David S. Matteson is Professor of Statistical Science at Cornell University and co-author of Statistics and Data Analysis for Financial Engineering with R examples.
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