TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Grimm, Florian
A1  - Kiefer, Daniel
A1  - Bauer, Markus
ED  - Zimmermann, Alfred
ED  - Howlett, Robert
ED  - Jain, Lakhmi
ED  - Schmidt, Rainer
T1  - Univariate time series forecasting by investigating intermittence and demand individually
T2  - Human centred intelligent systems : proceedings of KES-HCIS 2021 conference ; Smart innovation, systems and technologies, volume 244
N2  - 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.
KW  - univariate intermittent time series
KW  - artificial intelligence
KW  - forecasting
Y1  - 2021
SN  - 978-981-16-3266-2
SB  - 978-981-16-3266-2
U6  - https://doi.org/10.1007/978-981-16-3264-8_14
DO  - https://doi.org/10.1007/978-981-16-3264-8_14
SP  - 143
EP  - 151
S1  - 9
PB  - Springer
CY  - Singapore
ER  -