Teaching plan for the course unit

(Short version)


Catalā English Close imatge de maquetació




General information


Course unit name: Time Series Analysis

Course unit code: 361233

Academic year: 2021-2022

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



-  Lecture with practical component

Face-to-face and online




-  IT-based class

Face-to-face and online



Supervised project


Independent learning




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





Reading and study resources

Consulteu la disponibilitat a CERCABIB


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

Catāleg UB  Enllaç

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

Catāleg UB  Enllaç
Accés consorciat per als usuaris de la UB via Springer  Enllaç

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

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

Catāleg UB  Enllaç

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

Catāleg UB  Enllaç