The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Applying LSTM to Time Series Predictable Through Time-Window Approaches
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Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Self-citation
Cited by
Deep Forest: Towards An Alternative to Deep Neural Networks
Zhou, Z.-H., & Feng, J. (2017) · Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/497 · Cited by 935
CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus Data
Hanselmann, M., Strauss, T., Dormann, K., & Ulmer, H. (2020) · IEEE Access · doi:10.1109/access.2020.2982544 · Cited by 268
A novel ensemble deep learning model for stock prediction based on stock prices and news
Li, Y., & Pan, Y. (2022) · International Journal of Data Science and Analytics · doi:10.1007/s41060-021-00279-9 · Cited by 247
Metadata
- Authors
- Gers, F. A., Eck, D., & Schmidhuber, J.
- Venue
- Perspectives in Neural Computing
- Publisher
- Springer London
- Published
- 2002
- Pages
- 193-200
- ISSN
- 1431-6854 (ISSN-L)
- OpenAlex
- W4236047370
- Cited by
- 247
- References
- 1
- FWCI
- 0.59
- Type
- book chapter
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Applying LSTM to Time Series Predictable Through Time-Window Approaches. https://app.cochat.ai/papers/10.1007/978-1-4471-0219-9_20
- The paper
- Gers, F. A., Eck, D., & Schmidhuber, J. (2002). Applying LSTM to Time Series Predictable Through Time-Window Approaches. Perspectives in Neural Computing. https://doi.org/10.1007/978-1-4471-0219-9_20
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.
