79190 - Time Series

Learning outcomes

By the end of the course the student should know the fundamental theory of time series analysis. In particular the student should be able: - to analyse a time series in the time and in the frequency domain - to identify the stochastic process that has generated a time series based on the autocorrelation structure - to estimate and make inference on the parameters of a linear model for a stationary time series - to estimate time series components such as trend and seasonality by means of non parametric and parametric methods - to recognise the most important models for time series data

Course contents

The course covers the following topics. Linear models for time series data: linear processes, autoregressive unintegrated moving average processes (ARIMA) , seasonal processes. Identification, estimation and forecasting from ARIMA models. Time series decomposition. Time and frequency domain analysis.

 

Readings/Bibliography

Textbooks:

Brockwell P.J. and Davis R.A. (2002), Introduction to Time Series and Forecasting, Springer

Further readings:

Brockwell P.J. and Davis R.A. (1991). Time Series: Theory and Methods. Springer

 

 

Teaching methods

Recorded lectures, in-class lectures and discussions, exercises, laboratory.

Assessment methods

1) written exam  

2) case study analysis (coursework, take home)

Teaching tools

Textbook, notes and papers that can be found on the institutional teacher web-site and in Virtuale.

Office hours

See the website of Alessandra Luati