Schirmer, P. A., & Mporas, I. (2024). Py DTS: A Python Toolkit for Deep Learning Time Series Modelling. Entropy, 26(4), 311-333. https://doi.org/10.3390/e26040311
ISO-690 (author-date, English)SCHIRMER, Pascal A. und MPORAS, Iosif, 2024. Py DTS: A Python Toolkit for Deep Learning Time Series Modelling. Entropy. 1 April 2024. Vol. 26, no. 4, p. 311-333. DOI 10.3390/e26040311.
Modern Language Association 9th editionSchirmer, P. A., und I. Mporas. „Py DTS: A Python Toolkit for Deep Learning Time Series Modelling.“. Entropy, Bd. 26, Nr. 4, April 2024, S. 311-33, https://doi.org/10.3390/e26040311.
Mohr Siebeck - Recht (Deutsch - Österreich)Schirmer, Pascal A./Mporas, Iosif: Py DTS: A Python Toolkit for Deep Learning Time Series Modelling., Entropy 2024, 311-333.
Emerald - HarvardSchirmer, P.A. und Mporas, I. (2024), „Py DTS: A Python Toolkit for Deep Learning Time Series Modelling.“, Entropy, Vol. 26 No. 4, S. 311-333.