Combining Economic and Search-Request Variables to Predict Local Airline Market Shares: A Comparison of Forecasting Methods

2020 ◽  
Author(s):  
Paul Chiambaretto ◽  
Guillaume Coqueret
2013 ◽  
pp. 109-128 ◽  
Author(s):  
C. Rühl

This paper presents the highlights of the third annual edition of the BP Energy Outlook, which sets out BP’s view of the most likely developments in global energy markets to 2030, based on up-to-date analysis and taking into account developments of the past year. The Outlook’s overall expectation for growth in global energy demand is to be 36% higher in 2030 than in 2011 and almost all the growth coming from emerging economies. It also reflects shifting expectations of the pattern of supply, with unconventional sources — shale gas and tight oil together with heavy oil and biofuels — playing an increasingly important role and, in particular, transforming the energy balance of the US. While the fuel mix is evolving, fossil fuels will continue to be dominant. Oil, gas and coal are expected to converge on market shares of around 26—28% each by 2030, and non-fossil fuels — nuclear, hydro and renewables — on a share of around 6—7% each. By 2030, increasing production and moderating demand will result in the US being 99% self-sufficient in net energy. Meanwhile, with continuing steep economic growth, major emerging economies such as China and India will become increasingly reliant on energy imports. These shifts will have major impacts on trade balances.


Author(s):  
V. V. Nefedev

For the definition and implementation of breakthrough technologies the most important is the role of scientific and technical forecasting. Well-known forecasting methods based on extrapolation, expert assessments and mathematical modeling are not universal and have a number of significant disadvantages. The article proposes an original method of scientific and technical forecasting based on the use of the methodology of artificial neural networks. 


2019 ◽  
Vol 84 ◽  
pp. 01004 ◽  
Author(s):  
Grzegorz Dudek

The Theta method attracted the attention of researchers and practitioners in recent years due to its simplicity and superior forecasting accuracy. Its performance has been confirmed by many empirical studies as well as forecasting competitions. In this article the Theta method is tested in short-term load forecasting problem. The load time series expressing multiple seasonal cycles is decomposed in different ways to simplify the forecasting problem. Four variants of input data definition are considered. The standard Theta method is uses as well as the dynamic optimised Theta model proposed recently. The performances of the Theta models are demonstrated through an empirical application using real power system data and compared with other popular forecasting methods.


Author(s):  
Jonathan Coignard ◽  
Maxime Janvier ◽  
Vincent Debusschere ◽  
Gilles Moreau ◽  
Stéphanie Chollet ◽  
...  

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