Efficient Automated Deep Learning for Time Series Forecasting-Reference-Cited by-同舟云学术

Efficient Automated Deep Learning for Time Series Forecasting

Published:2023 Issue: Volume: Page:664-680
ISSN:0302-9743
Container-title:Machine Learning and Knowledge Discovery in Databases
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Author:

Deng Difan,Karl Florian,Hutter Frank,Bischl Bernd,Lindauer Marius

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-26409-2_40

Reference63 articles.

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3. Assimakopoulos, V., Nikolopoulos, K.: The theta model: a decomposition approach to forecasting. Int. J. Forecast. 16(4), 521–530 (2000)

4. Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv:1803.01271 (2018)

5. Beitner, J.: PyTorch forecasting: time series forecasting with PyTorch (2020)

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