Interactive Course

Time Series Analysis in R

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

  • 4 hours
  • 16 Videos
  • 58 Exercises
  • 31,074 Participants
  • 4,600 XP

Loved by learners at thousands of top companies:

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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

    Free

    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.

  2. 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.

  3. A simple moving average

    In this chapter, you will learn the simple moving average (MA) model and several of its basic properties. You will also practice simulating and estimating the MA model in R, and compare the MA model with the autoregressive (AR) model.

  4. Predicting the future

    In this chapter, you will conduct some trend spotting, and learn the white noise (WN) model, the random walk (RW) model, and the definition of stationary processes.

  5. Autoregression

    In this chapter, you will learn the autoregressive (AR) model and several of its basic properties. You will also practice simulating and estimating the AR model in R, and compare the AR model with the random walk (RW) model.

  1. 1

    Exploratory time series data analysis

    Free

    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.

  2. Predicting the future

    In this chapter, you will conduct some trend spotting, and learn the white noise (WN) model, the random walk (RW) model, and the definition of stationary processes.

  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.

  4. Autoregression

    In this chapter, you will learn the autoregressive (AR) model and several of its basic properties. You will also practice simulating and estimating the AR model in R, and compare the AR model with the random walk (RW) model.

  5. A simple moving average

    In this chapter, you will learn the simple moving average (MA) model and several of its basic properties. You will also practice simulating and estimating the MA model in R, and compare the MA model with the autoregressive (AR) model.

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Lloyd's Banking Group

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Harvard Business School

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Decision Science Analytics @ USAA

David S. Matteson
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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Collaborators
  • Lore Dirick

    Lore Dirick

  • Matt Isaacs

    Matt Isaacs

Prerequisites
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