Forecasting of location occupancy by means of cell phone network data

Alabort Medina, Arnau (2019). Forecasting of location occupancy by means of cell phone network data. Thesis (Master thesis), E.T.S. de Ingenieros Informáticos (UPM).

Description

Title: Forecasting of location occupancy by means of cell phone network data
Author/s:
  • Alabort Medina, Arnau
Contributor/s:
  • Solin, Arno
Item Type: Thesis (Master thesis)
Masters title: Data Science
Date: 21 September 2019
Subjects:
Freetext Keywords: Time series; Forecasting; Data science; Machine learning
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

This thesis examines the feasibility of building a forecasting model capable to predict the future location occupancy for different places in the UK. For this matter, historic data of the number of people in each location as well as other data sources during the same period of time have been used. Existing literature has researched the performance of different types of predictive models for time series. Some studies, with datasets from different sources and different time intervals, have been carried out at academia. After these studies, experts cannot agree on one best model and they tend to say that the performance of the models depends on the characteristics of the particular signal to forecast. In this thesis, a set of time series forecasting models have been tested on different time series signals corresponding to different locations. The code implemented provides an interface for the user to select the locations, models, and several other forecasting options in order to perform a proper evaluation of the predictions. Results obtained are not biased from what could be expected from the beginning, and the outcomes obtained with the small amount of data available tend to suggest that there is no single best model for all the different predicted locations. The use of data transformations and external regressors have also been tested in order to improve the performance of the models. Although some combinations of regressors and data transformations seem to improve some of the models, no promising conclusions can be concluded since this improvement does not apply for all the cases.

More information

Item ID: 57346
DC Identifier: http://oa.upm.es/57346/
OAI Identifier: oai:oa.upm.es:57346
Deposited by: Biblioteca Facultad de Informatica
Deposited on: 19 Nov 2019 11:20
Last Modified: 19 Nov 2019 11:20
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