The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Improving time series forecasting using LSTM and attention models
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
1 retracted
Sangeetha, K., & Prabha, D. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-01791-9 · Cited by 103
RetractedHochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Learning long-term dependencies with gradient descent is difficult
Bengio, Y., Simard, P., & Frasconi, P. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.279181 · Cited by 8,542
Time series forecasting using a hybrid ARIMA and neural network model
Zhang, G. P. (2003) · Neurocomputing · doi:10.1016/s0925-2312(01)00702-0 · Cited by 4,428
Deep learning with long short-term memory networks for financial market predictions
Fischer, T., & Krauss, C. (2018) · European Journal of Operational Research · doi:10.1016/j.ejor.2017.11.054 · Cited by 2,785
Attention-Based Models for Speech Recognition
Chorowski, J., Bahdanau, D., Serdyuk, D., Cho, K., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1506.07503 · Cited by 2,021
Brochu, E., Cora, V. M., & de Freitas, N. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1012.2599 · Cited by 2,011
Statistical Comparisons of Classifiers over Multiple Data Sets
DemšarJanez (2006) · Journal of Machine Learning Research · doi:10.5555/1248547.1248548 · Cited by 1,074
Time series forecasting of petroleum production using deep LSTM recurrent networks
Sagheer, A., & Kotb, M. (2019) · Neurocomputing · doi:10.1016/j.neucom.2018.09.082 · Cited by 968
Prechelt, L. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_5 · Cited by 951
Cited by
Prediction of Heat and Cold Loads of Factory Mushroom Houses Based on EWT Decomposition
Zuo, H., Zheng, W., Wang, M., & Zhang, X. (2023) · Sustainability · doi:10.3390/su152115270 · Cited by 8
Metadata
- Authors
- Abbasimehr, H., & Paki, R.
- Venue
- Journal of Ambient Intelligence and Humanized Computing
- Published
- 2021-01-03
- Volume
- 13
- Issue
- 1
- Pages
- 673-691
- ISSN
- 1868-5137 (ISSN-L)
- 1868-5145
- OpenAlex
- W3119095567
- Cited by
- 312
- References
- 41
- FWCI
- 18.75
- Type
- journal article
- 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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Improving time series forecasting using LSTM and attention models. https://app.cochat.ai/papers/10.1007/s12652-020-02761-x
- The paper
- Abbasimehr, H., & Paki, R. (2022). Improving time series forecasting using LSTM and attention models. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-020-02761-x
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.
