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Univariate time series forecasting by investigating intermittence and demand individually

  • Intermittent time series forecasting is a challenging task which still needs particular attention of researchers. The more unregularly events occur, the more difficult is it to predict them. With Croston’s approach in 1972 (1.Nr. 3:289–303), intermittence and demand of a time series were investigated the first time separately. He proposes an exponential smoothing in his attempt to generate a forecast which corresponds to the demand per period in average. Although this algorithm produces good results in the field of stock control, it does not capture the typical characteristics of intermittent time series within the final prediction. In this paper, we investigate a time series’ intermittence and demand individually, forecast the upcoming demand value and inter-demand interval length using recent machine learning algorithms, such as long-short-term-memories and light-gradient-boosting machines, and reassemble both information to generate a prediction which preserves the characteristics of an intermittent time series. We compare the results against Croston’s approach, as well as recent forecast procedures where no split is performed.

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Metadaten
Author of HS ReutlingenGrimm, Florian; Kiefer, Daniel
Editor of HS Reutlingen:Zimmermann, Alfred
DOI:https://doi.org/10.1007/978-981-16-3264-8_14
ISBN:978-981-16-3266-2
Erschienen in:Human centred intelligent systems : proceedings of KES-HCIS 2021 conference ; Smart innovation, systems and technologies, volume 244
Publisher:Springer
Place of publication:Singapore
Editor:Alfred ZimmermannORCiD, Robert Howlett, Lakhmi Jain, Rainer Schmidt
Document Type:Conference proceeding
Language:English
Publication year:2021
Tag:artificial intelligence; forecasting; univariate intermittent time series
Page Number:9
First Page:143
Last Page:151
DDC classes:004 Informatik
Open access?:Nein
Licence (German):License Logo  In Copyright - Urheberrechtlich geschützt