课程

ARIMA Models in R

基础技能水平

更新时间 2024年8月

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

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RProbability & Statistics4 小时13 视频45 练习3,600 经验值34,719成就声明

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课程描述

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.

先决条件

Time Series Analysis in R

1

Time Series Data and Models

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.

First things first

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Elements of time series

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Stationarity and nonstationarity

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Differencing

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Detrending data

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Dealing with trend and heteroscedasticity

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Stationary time series: ARMA

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Simulating ARMA models

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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.

AR and MA models

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Fitting an AR(1) model

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Fitting an AR(2) model

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Fitting an MA(1) model

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AR and MA together

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Fitting an ARMA model

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Identify an ARMA model

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Model choice and residual analysis

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Model choice - I

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Model choice - II

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Residual analysis - I

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Residual analysis - II

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ARMA get in

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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.

ARIMA - integrated ARMA

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ARIMA - plug and play

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Simulated ARIMA

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Global warming

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ARIMA diagnostics

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Diagnostics - simulated overfitting

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Diagnostics - global temperatures

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Forecasting ARIMA

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Forecasting simulated ARIMA

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Forecasting global temperatures

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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.

Pure seasonal models

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P/ACF of pure seasonal models

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Fit a pure seasonal model

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Mixed seasonal models

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Fit a mixed seasonal model

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Data analysis - unemployment I

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Data analysis - unemployment II

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Data analysis - commodity prices

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Data analysis - birth rate

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Forecasting seasonal ARIMA

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Forecasting monthly unemployment

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How hard is it to forecast commodity prices?

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Congratulations!

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ARIMA Models in R

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