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Hybrid Methods for Time Series Forecasting

Berberich, Daniel


Time series forecasting is a crucial task in various fields of business and science. There are two coexisting approaches to time series forecasting, which are statistical methods and machine learning methods. Both come with different strengths and limitations. Statistical methods such as the Holt-Winters’ Method or ARIMA have been practiced for decades. They stand out due to their robustness and flexibility. Furthermore, these methods work well when few data is available and can exploit a priori knowledge. However, statistical methods assume linear relationships in the data, which is not necessarily the case in real-world data, inhibiting forecasting performance.
On the other hand, machine learning methods such as Multilayer Perceptrons or Long Short-Term Memory Networks do not have the assumption of linearity and have the exceptional advantage of universally approximating almost any function. In addition to that, machine learning methods can exploit cross-series information to enhance an individual forecast. Besides these strengths, machine learning methods face several limitations in terms of data and computation requirements.
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Volltext §
DOI: 10.5445/IR/1000134512
Veröffentlicht am 29.06.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Karlsruhe Service Research Institute (KSRI)
Publikationstyp Hochschulschrift
Publikationsdatum 20.06.2020
Sprache Englisch
Identifikator KITopen-ID: 1000134512
Verlag Karlsruher Institut für Technologie (KIT)
Umfang X, 106 S.
Art der Arbeit Abschlussarbeit - Master
Prüfungsdaten 20.06.2020
Referent/Betreuer Fromm, Hansjörg
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