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ARIMA Models in R

Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.

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4 Stunden13 Videos45 Übungen31.845 LernendeTrophyLeistungsnachweis

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Kursbeschreibung

In this course, you will become an expert in fitting ARIMA models to time series data using R. First, you will explore the nature of time series data using the tools in the R stats package. Next, you learn how to fit various ARMA models to simulated data (where you will know the correct model) using the R package astsa. Once you have mastered the basics, you will learn how to fit integrated ARMA models, or ARIMA models to various real data sets. You will learn how to check the validity of an ARIMA model and you will learn how to forecast time series data. Finally, you will learn how to fit ARIMA models to seasonal data, including forecasting using the astsa package.
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In den folgenden Tracks

Quantitativer Analyst mit R

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Zeitreihen mit R

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  1. 1

    Time Series Data and Models

    Kostenlos

    You will investigate the nature of time series data and learn the basics of ARMA models that can explain the behavior of such data. You will learn the basic R commands needed to help set up raw time series data to a form that can be analyzed using ARMA models.

    Kapitel Jetzt Abspielen
    First things first
    50 xp
    Data play
    100 xp
    Elements of time series
    50 xp
    Stationarity and nonstationarity
    50 xp
    Differencing
    100 xp
    Detrending data
    100 xp
    Dealing with trend and heteroscedasticity
    100 xp
    Stationary time series: ARMA
    50 xp
    Simulating ARMA models
    100 xp
  2. 2

    Fitting ARMA models

    You will discover the wonderful world of ARMA models and how to fit these models to time series data. You will learn how to identify a model, how to choose the correct model, and how to verify a model once you fit it to data. You will learn how to use R time series commands from the stats and astsa packages.

    Kapitel Jetzt Abspielen
    AR and MA models
    50 xp
    Fitting an AR(1) model
    100 xp
    Fitting an AR(2) model
    100 xp
    Fitting an MA(1) model
    100 xp
    AR and MA together
    50 xp
    Fitting an ARMA model
    100 xp
    Identify an ARMA model
    50 xp
    Model choice and residual analysis
    50 xp
    Model choice - I
    100 xp
    Model choice - II
    50 xp
    Residual analysis - I
    100 xp
    Residual analysis - II
    50 xp
    ARMA get in
    100 xp
  3. 3

    ARIMA Models

    Now that you know how to fit ARMA models to stationary time series, you will learn about integrated ARMA (ARIMA) models for nonstationary time series. You will fit the models to real data using R time series commands from the stats and astsa packages.

    Kapitel Jetzt Abspielen
    ARIMA - integrated ARMA
    50 xp
    ARIMA - plug and play
    100 xp
    Simulated ARIMA
    100 xp
    Global warming
    100 xp
    ARIMA diagnostics
    50 xp
    Diagnostics - simulated overfitting
    100 xp
    Diagnostics - global temperatures
    100 xp
    Forecasting ARIMA
    50 xp
    Forecasting simulated ARIMA
    100 xp
    Forecasting global temperatures
    100 xp
  4. 4

    Seasonal ARIMA

    You will learn how to fit and forecast seasonal time series data using seasonal ARIMA models. This is accomplished using what you learned in the previous chapters and by learning how to extend the R time series commands available in the stats and astsa packages.

    Kapitel Jetzt Abspielen
    Pure seasonal models
    50 xp
    P/ACF of pure seasonal models
    50 xp
    Fit a pure seasonal model
    100 xp
    Mixed seasonal models
    50 xp
    Fit a mixed seasonal model
    100 xp
    Data analysis - unemployment I
    100 xp
    Data analysis - unemployment II
    100 xp
    Data analysis - commodity prices
    100 xp
    Data analysis - birth rate
    100 xp
    Forecasting seasonal ARIMA
    50 xp
    Forecasting monthly unemployment
    100 xp
    How hard is it to forecast commodity prices?
    100 xp
    Congratulations!
    50 xp
Für Unternehmen

Trainierst du 2 oder mehr?

Verschaffen Sie Ihrem Team Zugriff auf die vollständige DataCamp-Plattform, einschließlich aller Funktionen.

In den folgenden Tracks

Quantitativer Analyst mit R

Gehe zu Track

Zeitreihen mit R

Gehe zu Track

Mitwirkende

Collaborator's avatar
Lore Dirick
Collaborator's avatar
Matt Isaacs

Voraussetzungen

Time Series Analysis in R
David Stoffer HeadshotDavid Stoffer

Professor of Statistics at the University of Pittsburgh

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