Fourier-based parametrization of convolutional neural networks for robust time series forecasting

DOI:	https://doi.org/10.1007/978-3-030-33778-0_39
URL:	https://link.springer.com/chapter/10.1007/978-3-03...
Additional URL:	http://www.heikopaulheim.com/docs/ds2019.pdf
Document Type:	Conference or workshop publication
Year of publication:	2019
Book title:	Discovery Science 22nd International Conference, DS 2019, Split, Croatia, October 28–30, 2019, Proceedings
The title of a journal, publication series:	Lecture Notes in Computer Science
Volume:	11828
Page range:	522-532
Conference title:	DS 2019
Location of the conference venue:	Split, Croatia
Date of the conference:	28.-30.10.2019
Publisher:	Kralj Novak, Petra
Place of publication:	Berlin [u.a.]
Publishing house:	Springer
ISBN:	978-3-030-33777-3 , 978-3-030-33778-0
ISSN:	0302-9743 , 1611-3349
Publication language:	English
Institution:	School of Business Informatics and Mathematics > Data Science (Paulheim 2018-)
Subject:	004 Computer science, internet
Abstract:	Classical statistical models for time series forecasting most often make a number of assumptions about the data at hand, there-with, requiring intensive manual preprocessing steps prior to modeling. As a consequence, it is very challenging to come up with a more generic forecasting framework. Extensive hyperparameter optimization and ensemble architectures are common strategies to tackle this problem, however, this comes at the cost of high computational complexity. Instead of optimizing hyperparameters by training multiple models, we propose a method to estimate optimal hyperparameters directly from the characteristics of the time series at hand. To that end, we use Convolutional Neural Networks (CNNs) for time series forecasting and determine a part of the network layout based on the time series’ Fourier coefficients. Our approach significantly reduces the amount of required model configuration time and shows competitive performance on time series data across various domains. A comparison to popular, state of the art forecasting algorithms reveals further improvements in runtime and practicability.