Teaching plan for the course unit

(Short version)

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 General information

Course unit name: Time Series Analysis

Course unit code: 361233

Coordinator: Jose Antonio Sanchez Espigares

Department: Faculty of Economics and Business

Credits: 6

Single program: S

 Estimated learning time Total number of hours 150

 Face-to-face and/or online activities 60
 -  Lecture with practical component Face-to-face and online 45 -  IT-based class Face-to-face and online 15
 Supervised project 40
 Independent learning 50

 Learning objectives
 Referring to knowledge — Know and understand the different methods used in the deterministic analysis of time series in order to make predictions and estimate their components.   — Know the theoretical and practical foundations associated with the identification, estimation, validation and modeling of time series using SARIMA models.  Referring to abilities, skills — Identify whether a time series follows an additive or multiplicative seasonality.   — Apply the methods of the deterministic analysis of time series in order to make predictions.   — Given a time series, be able to decide which type of SARIMA model is most appropriate.   — Use SARIMA models to make predictions.   — Use and program estimation and prediction algorithms using R.

 Teaching blocks

1. Introduction to time series

1.1. Definition of time series and economic forecasting

1.2. Classification of prediction methods

1.3. Predictive capacity evaluation criteria

2. Deterministic analysis of time series

2.1. Components of a time series

2.2. Prediction using non-trend models

2.3. Prediction with trend models

3. Deterministic treatment of seasonality

3.1. Analysis of the seasonal component

3.2. Prediction with non-trend models and seasonal component

3.3. Prediction with trend models and seasonal component

4. Stochastic analysis of time series

4.1. Stochastic processes

4.2. Concepts of stationarity and ergodicity

4.3. Autocovariance and autocorrelation functions

4.4. Autocovariance and sample autocorrelation functions

4.5. Elementary models: white noise and random path

5. Linear models of time series

5.1. Moving Average Models (MA)

5.2. Autoregressive models (AR)

5.3. Mixed models (ARMA)

5.4. Non-stationary processes; Integrated models (ARIMA)

5.5. Seasonal models (SARIMA)

6. Box-Jenkins Methodology

6.1. Identification of SARIMA models

6.2. Parameter estimation

6.3. Model validation

6.4. Point and interval prediction

Book

BOX, George E. P. et al. Time series analysis: forescasting and control. 4th edició. Hoboken, N.J.: Wiley 2008

BROCKWELL, Peter J. Introduction to time series and forecasting. New York: Springer, 2010

PEÑA, Daniel. Análisis de series temporales. Madrid: Alianza Editorial. 2010

SHUMWAY, Robert H. et. al. Time series analysis and Its applications: with R exemples. 3rd ed. New York [etc.]: Springer, 2017

URIEL, Ezequiel, et. al. Introducción al análisis de series temporales. Madrid: Editorial AC-Thomson, 2000