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

Data play

Elements of time series

Stationarity and nonstationarity

Differencing

Detrending data

Dealing with trend and heteroscedasticity

Stationary time series: ARMA

Simulating ARMA models

Time Series Data and 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.

Modelli AR e MA

Adattare un modello AR(1)

Adattare un modello AR(2)

Adattare un modello MA(1)

AR e MA insieme

Adattare un modello ARMA

Identifica un modello ARMA

Scelta del modello e analisi dei residui

Scelta del modello - I

Scelta del modello - II

Analisi dei residui - I

Analisi dei residui - II

ARMA al via

Fitting ARMA 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

ARIMA - plug and play

Simulated ARIMA

Global warming

ARIMA diagnostics

Diagnostics - simulated overfitting

Diagnostics - global temperatures

Forecasting ARIMA

Forecasting simulated ARIMA

Forecasting global temperatures

ARIMA Models

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

P/ACF of pure seasonal models

Fit a pure seasonal model

Mixed seasonal models

Fit a mixed seasonal model

Data analysis - unemployment I

Data analysis - unemployment II

Data analysis - commodity prices

Data analysis - birth rate

Forecasting seasonal ARIMA

Forecasting monthly unemployment

How hard is it to forecast commodity prices?

Congratulations!

Seasonal ARIMA

In questo corso diventerai esperto nell’adattare modelli ARIMA a serie temporali usando R. Per iniziare, esplorerai la natura dei dati di serie temporali con gli strumenti del pacchetto stats di R. Poi imparerai a adattare vari modelli ARMA a dati simulati (dove conoscerai il modello corretto) usando il pacchetto R astsa. Dopo aver consolidato le basi, vedrai come adattare modelli ARMA integrati, cioè modelli ARIMA, a diversi insiemi di dati reali. Imparerai a verificare la validità di un modello ARIMA e a effettuare previsioni su serie temporali. Infine, imparerai come adattare modelli ARIMA a dati stagionali, includendo le previsioni con il pacchetto astsa.

Time Series Analysis in R

In questo corso diventerai esperto nell'adattare modelli ARIMA a serie temporali con R e astsa.

Modelli ARIMA in R

Diventa un esperto nell'applicazione dei modelli ARIMA (media mobile autoregressiva integrata) ai dati delle serie temporali usando R.

Analista quantitativo  in R

Serie temporali  in R

Scelta del modello - II

Modelli ARIMA in R

Esercizio pratico interattivo