• norsk
    • English
  • norsk 
    • norsk
    • English
  • Logg inn
Vis innførsel 
  •   Hjem
  • Norges Handelshøyskole
  • Thesis
  • Master Thesis
  • Vis innførsel
  •   Hjem
  • Norges Handelshøyskole
  • Thesis
  • Master Thesis
  • Vis innførsel
JavaScript is disabled for your browser. Some features of this site may not work without it.

Machine learning in the aviation industry and the potential of using air traffic as a real-time indicator of GDP : a study of how useful machine learning is to predict Norwegian air traffic and investigating the causal relationship between air traffic and GDP

Lohne, Andrea Madeleine; Skrbo, Nejira
Master thesis
Thumbnail
Åpne
masterthesis.pdf (1.958Mb)
Permanent lenke
https://hdl.handle.net/11250/2735120
Utgivelsesdato
2020
Metadata
Vis full innførsel
Samlinger
  • Master Thesis [4657]
Sammendrag
Travel by air is an essential part of both the Norwegian society and its infrastructure,

where Norway has one of the highest number of flights per capita in Europe. Nonetheless,

the aviation industry is characterized by high uncertainty, with the Covid-19 pandemic

being the most recent one.

This thesis has sought to investigate the use of machine learning in the Norwegian aviation

industry and how the number of air passengers potentially can be used as a real-time

indicator of GDP. Therefore, the thesis has been divided into two parts. The first part has

aimed to use machine learning to predict the number of domestic and total passengers per

capita in Norway. More precisely, we applied the methods OLS, elastic net, and random

forest. The purpose of the second part has been to investigate the causal relationship

between air passengers and GDP by conducting a strict linear Granger causality test. We

particularly questioned whether air passengers could be used as a real-time indicator of

GDP.

The findings suggest that machine learning is applicable for predicting the number of air

passengers per capita in Norway, where elastic net yield the best results. In relation to

the second part of the thesis, the findings reveal a causal relationship running from air

passengers to GDP. Consequently, we find that there is a potential of using the number of

air passengers as a real-time indicator of GDP in Norway.

Keywords – Machine learning, the Norwegian aviation industry, economic growth,

causality, real-time indicators

Kontakt oss | Gi tilbakemelding

Personvernerklæring
DSpace software copyright © 2002-2019  DuraSpace

Levert av  Unit
 

 

Bla i

Hele arkivetDelarkiv og samlingerUtgivelsesdatoForfattereTitlerEmneordDokumenttyperTidsskrifterDenne samlingenUtgivelsesdatoForfattereTitlerEmneordDokumenttyperTidsskrifter

Min side

Logg inn

Statistikk

Besøksstatistikk

Kontakt oss | Gi tilbakemelding

Personvernerklæring
DSpace software copyright © 2002-2019  DuraSpace

Levert av  Unit