ReCycle: Fast and Efficient Long Time Series Forecasting with Residual Cyclic Transformers
Author:
Affiliation:
1. Karlsruhe Institute of Technology (KIT),Karlsruhe,Gemany
2. German Aerospace Center (DLR),Oldenburg,Germany
3. INENSUS GmbH,Goslar,Germany
4. Siemens AG, Digital Industries,Karlsruhe,Germany
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10605128/10605229/10605307.pdf?arnumber=10605307
Reference17 articles.
1. An Experimental Review on Deep Learning Architectures for Time Series Forecasting
2. Attention Is All You Need;Vaswani,2017
3. Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
4. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting;Wu;Advances in Neural Information Processing Systems,2021
5. FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting;Zhou
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