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TIME SERIES FORECASTING: COMPARISON OF BOX-JENKINS, HYBRID AND MACHINE LEARNING METHODS

Gueulette, Arnaud
(2023)

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GUEULETTE_10511601_2023.pdf
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Details

Supervisors
von Sachs, Rainer
Faculty
Faculté des sciences
Degree label
Master [120] en science des données, orientation statistique, à finalité spécialisée
Abstract
This thesis mainly discusses forecasting competitions and how they contribute to the field of forecasting. It also discusses the winner of the M4 competition, a hybrid model that blends statistical and deep learning concepts. The model, called Exponential Smoothing Recurrent Neural Network (ES-RNN), was proposed by Slawek Smyl, a data scientist at Uber. This hybrid model outperformed all other models in the M4 competition, pointing the way for further development and research into more complex methods.

Citations

  • APA

Gueulette, A. (2023). TIME SERIES FORECASTING: COMPARISON OF BOX-JENKINS, HYBRID AND MACHINE LEARNING METHODS. https://dial-mem.test.bib.ucl.ac.be/handle/2078.2/35256