The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Study and analysis of SARIMA and LSTM in forecasting time series data
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Citations
Cites
Time Series Analysis: Forecasting and Control.
Anderson, O. D., Box, G. E. P., & Jenkins, G. M. (1978) · The Statistician · doi:10.2307/2988198 · Cited by 5,298
Time Series Analysis, Forecasting and Control.
Kendall, M. G., Box, G. E. P., & Jenkins, G. M. (1971) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2344246 · Cited by 2,771
A Comparison of ARIMA and LSTM in Forecasting Time Series
Siami-Namini, S., Tavakoli, N., & Siami Namin, A. (2018) · 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) · doi:10.1109/icmla.2018.00227 · Cited by 1,387
An Information Framework for Creating a Smart City Through Internet of Things
Jin, J., Gubbi, J., Marusic, S., & Palaniswami, M. (2014) · IEEE Internet of Things Journal · doi:10.1109/jiot.2013.2296516 · Cited by 1,320
A survey on Advanced Metering Infrastructure
Rashed Mohassel, R., Fung, A., Mohammadi, F., & Raahemifar, K. (2014) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2014.06.025 · Cited by 632
Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey
Alahakoon, D., & Yu, X. (2016) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2015.2414355 · Cited by 596
Deep Learning: A Practitioner's Approach
Patterson, J., & Adam, G. (2017) · Cited by 486
Energy meters evolution in smart grids: A review
Avancini, D. B., Rodrigues, J. J. P. C., Martins, S. G. B., Rabêlo, R. A. L., Al-Muhtadi, J., & Solic, P. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.01.229 · Cited by 323
Time Series Forecasting of Temperatures using SARIMA: An Example from Nanjing
Chen, P., Niu, A., Liu, D., Jiang, W. A., & Ma, B. (2018) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/394/5/052024 · Cited by 146
High-pass filtering artifacts in multivariate classification of neural time series data
van Driel, J., Olivers, C. N. L., & Fahrenfort, J. J. (2021) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2021.109080 · Cited by 142
Cited by
Karijadi, I., & Chou, S.-Y. (2022) · Energy and Buildings · doi:10.1016/j.enbuild.2022.111908 · Cited by 159
(2023) · doi:10.5194/hess-27-83-2023 · Cited by 51
Metadata
- Authors
- Kumar Dubey, A., Kumar, A., García-Díaz, V., Kumar Sharma, A., & Kanhaiya, K.
- Venue
- Sustainable Energy Technologies and Assessments
- Publisher
- Elsevier BV
- Published
- 2021-10
- Volume
- 47
- Pages
- 101474
- ISSN
- 2213-1388 (ISSN-L)
- OpenAlex
- W3185274067
- Cited by
- 357
- References
- 31
- FWCI
- 13.11
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Kumar Dubey, A., Kumar, A., García-Díaz, V., Kumar Sharma, A., & Kanhaiya, K. (2021). Study and analysis of SARIMA and LSTM in forecasting time series data. Sustainable Energy Technologies and Assessments. https://doi.org/10.1016/j.seta.2021.101474
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.
