The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting
Abstract
Abstract This paper presents the winning submission of the M4 forecasting competition. The submission utilizes a dynamic computational graph neural network system that enables a standard exponential smoothing model to be mixed with advanced long short term memory networks into a common framework. The result is a hybrid and hierarchical forecasting method.
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Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
The forthcoming artificial intelligence (AI) revolution: Its impact on society and firms
Makridakis, S. (2017) · Futures · doi:10.1016/j.futures.2017.03.006 · Cited by 1,866 · cited 1×
“Over recent decades, neural networks (NNs) and other machine learning (ML) algorithms have achieved remarkable success in various areas, including image and speech recognition, natural language processing (NLP), autonomous vehicles and games (Makridakis, 2017), among others.”
Electricity price forecasting: A review of the state-of-the-art with a look into the future
Weron, R. (2014) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2014.08.008 · Cited by 1,646
Cited by
Recurrent Neural Networks for Time Series Forecasting: Current status and future directions
Hewamalage, H., Bergmeir, C., & Bandara, K. (2021) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2020.06.008 · Cited by 1,371
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
Benidis, K., Rangapuram, S. S., Flunkert, V., Wang, Y., Maddix, D., Turkmen, C., Gasthaus, J., Bohlke-Schneider, M., Salinas, D., Stella, L., et al. (2023) · ACM Computing Surveys · doi:10.1145/3533382 · Cited by 382
Why we need biased AI: How including cognitive biases can enhance AI systems
Hagendorff, T., & Fabi, S. (2024) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813x.2023.2178517 · Cited by 20
Metadata
- Authors
- Smyl, S.
- Venue
- International Journal of Forecasting
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 36
- Issue
- 1
- Pages
- 75-85
- ISSN
- 0169-2070 (ISSN-L)
- OpenAlex
- W2962752580
- Cited by
- 761
- References
- 3
- FWCI
- 50.80
- Type
- journal article
- Language
- en
- Open access
- closed
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- Smyl, S. (2020). A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting. International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2019.03.017
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
