# name
Introduction to time series and forecasting

# metadata.dc.description[].value
Treballs Finals del Doble Grau d'Administració i Direcció d'Empreses i de Matemàtiques, Facultat d'Economia i Empresa i Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Curs: 2021-2022, Tutor: 	
Josep Vives i Santa Eulàlia i F. Javier Martínez de Albéniz

# metadata.dc.description.abstract[].value
[en] Time series analysis allows complex processes to be expressed in simple terms to understand how these processes were generated and to predict future values. SARIMA models assume that the observations of a process depend on the previous observations and the variation between them to give an expression of the underlying data generating process.
To find the SARIMA model that better fits our data we introduce the Box and Jenkins method, based on three iterative steps: model identification, parameter estimation and fitness check. Once we have identified the most appropriate fitting model, we use it to forecast future values.
We have followed this methodology to find the model that best fits the Spanish unemployment series from 2002 to the first quarter of 2022 and to forecast the next 8 observations.

# metadata.dc.rights[].value
cc-by-nc-nd (c) Eloi Castaño Camps, 2022

# metadata.dc.source[].value
Treballs Finals de Grau (TFG) - Administració i Direcció d’Empreses i Matemàtiques (Doble Grau)

# metadata.dc.title[].value
Introduction to time series and forecasting