{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T21:17:58Z","timestamp":1784323078605,"version":"3.55.0"},"reference-count":54,"publisher":"Polish Information Processing Society","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"DOI":"10.15439\/2025f7041","type":"proceedings-article","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T07:44:23Z","timestamp":1761119063000},"page":"265-276","source":"Crossref","is-referenced-by-count":2,"title":["A Comparative Study of LSTM Efficiency vs. Transformer Power for Localized Time Series Forecasting"],"prefix":"10.15439","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-6872-358X","authenticated-orcid":true,"given":"Twisampati","family":"Sarkar","sequence":"first","affiliation":[{"name":"University of Louisiana of Lafayette"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5817-8798","authenticated-orcid":true,"given":"Henry","family":"Chu","sequence":"additional","affiliation":[{"name":"University of Louisiana at Lafayette"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"6175","published-online":{"date-parts":[[2025,10,15]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","unstructured":"Y. C. Fung and B. Amonov, \u201cDecoding financial data: Machine\nlearning approach to predict trading actions,\u201d in Proceedings of\nthe 19th Conference on Computer Science and Intelligence Systems\n(FedCSIS), ser. Annals of Computer Science and Information\nSystems, M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki,\nand D. \u015al\u0119zak, Eds., vol. 39. IEEE, 2024, p. 739\u2013744. [Online].\nAvailable: http:\/\/dx.doi.org\/10.15439\/2024F4556","DOI":"10.15439\/2024F4556"},{"key":"ref2","doi-asserted-by":"publisher","unstructured":"C. Lin, \u201cKey financial indicators analysis and stock trend forecasting\nbased on a wrapper feature selection method,\u201d in Proceedings\nof the 19th Conference on Computer Science and Intelligence\nSystems (FedCSIS), ser. Annals of Computer Science and Information\nSystems, M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki,\nand D. \u015al\u0119zak, Eds., vol. 39. IEEE, 2024, p. 755\u2013759. [Online].\nAvailable: http:\/\/dx.doi.org\/10.15439\/2024F3560","DOI":"10.15439\/2024F3560"},{"key":"ref3","doi-asserted-by":"publisher","unstructured":"V.-T. Duong, D.-T.-A. Nguyen, T.-T.-H. Pham, V.-H. Nguyen, and\nV.-Q. A. Le, \u201cComparative study of deep learning models for\npredicting stock prices,\u201d in Proceedings of the Seventh International\nConference on Research in Intelligent and Computing in Engineering,\nser. Annals of Computer Science and Information Systems, V. K.\nSolanki and B. T. Thanh, Eds., vol. 33. PTI, 2022, p. 103\u2013108.\n[Online]. Available: http:\/\/dx.doi.org\/10.15439\/2022R02","DOI":"10.15439\/2022R02"},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"J. Jenko, J. P. Costa, D. Vladu\u0161i\u010d, U. Bav\u010dar, and R. \u0160abarkapa,\n\u201cLearning from the COVID-19 pandemic to improve critical infrastructure resilience using temporal fusion transformers,\u201d in Proceedings\nof the 19th Conference on Computer Science and Intelligence Systems\n(FedCSIS), 2024, pp. 375\u2013384.","DOI":"10.15439\/2024F2959"},{"key":"ref5","doi-asserted-by":"publisher","unstructured":"S. Yang, M. Tan, S. Xia, and F. Liu, \u201cA method of intrusion\ndetection based on attention-lstm neural network,\u201d in Proceedings\nof the 2020 5th International Conference on Machine Learning\nTechnologies, ser. ICMLT 2020. New York, NY, USA: Association\nfor Computing Machinery, 2020, p. 46\u201350. [Online]. Available:\nhttps:\/\/doi.org\/10.1145\/3409073.3409096","DOI":"10.1145\/3409073.3409096"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"S. Ahmad, A. Lavin, S. Purdy, and Z. Agha, \u201cUnsupervised real-time\nanomaly detection for streaming data,\u201d Neurocomputing, vol. 262,\npp. 134\u2013147, 2017, online Real-Time Learning Strategies for Data\nStreams. [Online]. Available: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0925231217309864","DOI":"10.1016\/j.neucom.2017.04.070"},{"key":"ref7","unstructured":"P. Malhotra, A. Ramakrishnan, G. Anand, L. Vig, P. Agarwal,\nand G. Shroff, \u201cLSTM-based encoder-decoder for multi-sensor\nanomaly detection,\u201d Computing Research Repository (CoRR), vol.\nabs\/1607.00148, 2016. [Online]. Available: http:\/\/arxiv.org\/abs\/1607.00148"},{"key":"ref8","doi-asserted-by":"publisher","unstructured":"G. Lai, W.-C. Chang, Y. Yang, and H. Liu, \u201cModeling long- and\nshort-term temporal patterns with deep neural networks,\u201d in The\n41st International ACM SIGIR Conference on Research &\nDevelopment in Information Retrieval, ser. SIGIR \u201918. New York,\nNY, USA: Association for Computing Machinery, 2018, p. 95\u2013104.\n[Online]. Available: https:\/\/doi.org\/10.1145\/3209978.3210006","DOI":"10.1145\/3209978.3210006"},{"key":"ref9","doi-asserted-by":"publisher","unstructured":"J. Yin, W. Rao, M. Yuan, J. Zeng, K. Zhao, C. Zhang, J. Li, and\nQ. Zhao, \u201cExperimental study of multivariate time series forecasting\nmodels,\u201d in Proceedings of the 28th ACM International Conference on\nInformation and Knowledge Management, ser. CIKM \u201919. New York,\nNY, USA: Association for Computing Machinery, 2019, p. 2833\u20132839.\n[Online]. Available: https:\/\/doi.org\/10.1145\/3357384.3357826","DOI":"10.1145\/3357384.3357826"},{"key":"ref10","unstructured":"I. Sutskever, O. Vinyals, and Q. V. Le, \u201cSequence to sequence learning\nwith neural networks,\u201d in Advances in Neural Information Processing\nSystems, Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence,\nand K. Q. Weinberger, Eds., vol. 27. Curran Associates, Inc.,\n2014. [Online]. Available: https:\/\/proceedings.neurips.cc\/paper\/2014\/file\/a14ac55a4f27472c5d894ec1c3c743d2-Paper.pdf"},{"key":"ref11","doi-asserted-by":"publisher","unstructured":"S. Afreen, N. T. D. Linh, S. Kodur, and A. Begum,\n\u201cSeq2Seq transformer-based model for optimized Chinese-to-English\ntranslation,\u201d in Proceedings of the Ninth International Conference\non Research in Intelligent Computing in Engineering, ser. Annals of\nComputer Science and Information Systems, V. K. Solanki, T. D.\nTan, P. Kumar, and M. Cardona, Eds., vol. 42. PTI, 2024, p. 1\u201310.\n[Online]. Available: http:\/\/dx.doi.org\/10.15439\/2024R81","DOI":"10.15439\/2024R81"},{"key":"ref12","doi-asserted-by":"publisher","unstructured":"S. Hochreiter and J. Schmidhuber, \u201cLong Short-Term Memory,\u201d\nNeural Computation, vol. 9, no. 8, pp. 1735\u20131780, 11 1997. [Online].\nAvailable: https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref13","doi-asserted-by":"publisher","unstructured":"T. Markovic, A. Dehlaghi-Ghadim, M. Leon, A. Balador, and\nS. Punnekkat, \u201cTime-series anomaly detection and classification\nwith long short-term memory network on industrial manufacturing\nsystems,\u201d in Proceedings of the 18th Conference on Computer\nScience and Intelligence Systems, ser. Annals of Computer Science\nand Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki,\nand D. \u015al\u0119zak, Eds., vol. 35. IEEE, 2023, p. 171\u2013181. [Online].\nAvailable: http:\/\/dx.doi.org\/10.15439\/2023F5263","DOI":"10.15439\/2023F5263"},{"key":"ref14","doi-asserted-by":"publisher","unstructured":"P. Lam, L. Pham, T. Nguyen, H. Tang, M. Seidl, M. Andresel,\nand A. Schindler, \u201cLSTM-based deep neural network with a focus\non sentence representation for sequential sentence classification\nin medical scientific abstracts,\u201d in Proceedings of the 19th\nConference on Computer Science and Intelligence Systems (FedCSIS),\nser. Annals of Computer Science and Information Systems,\nM. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, and\nD. \u015al\u02dbezak, Eds., vol. 39. IEEE, 2024, p. 219\u2013224. [Online].\nAvailable: http:\/\/dx.doi.org\/10.15439\/2024F5872","DOI":"10.15439\/2024F5872"},{"key":"ref15","unstructured":"A. Vaswani, N. M. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N.\nGomez, L. Kaiser, and I. Polosukhin, \u201cAttention is all you need,\u201d in\nNeural Information Processing Systems, 2017."},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"T. Luong, H. Pham, and C. D. Manning, \u201cEffective approaches\nto attention-based neural machine translation,\u201d in Proceedings of\nthe 2015 Conference on Empirical Methods in Natural Language\nProcessing. Lisbon, Portugal: Association for Computational\nLinguistics, Sep. 2015, pp. 1412\u20131421. [Online]. Available: https:\/\/www.aclweb.org\/anthology\/D15-1166","DOI":"10.18653\/v1\/D15-1166"},{"key":"ref17","unstructured":"D. Bahdanau, K. Cho, and Y. Bengio, \u201cNeural machine translation\nby jointly learning to align and translate,\u201d in Proceedings of the 3rd\nInternational Conference on Learning Representations, ICLR 2015,\nJan. 2015, 3rd International Conference on Learning Representations,\nICLR 2015 ; Conference date: 07-05-2015 Through 09-05-2015."},{"key":"ref18","doi-asserted-by":"crossref","unstructured":"K. Cho, B. van Merri\u00ebnboer, C. Gulcehre, D. Bahdanau, F. Bougares,\nH. Schwenk, and Y. Bengio, \u201cLearning phrase representations\nusing RNN encoder\u2013decoder for statistical machine translation,\u201d\nin Proceedings of the 2014 Conference on Empirical Methods in\nNatural Language Processing (EMNLP). Doha, Qatar: Association\nfor Computational Linguistics, Oct. 2014, pp. 1724\u20131734. [Online].\nAvailable: https:\/\/www.aclweb.org\/anthology\/D14-1179","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref19","unstructured":"H. Wu, J. Xu, J. Wang, and M. Long, \u201cAutoformer: Decomposition\ntransformers with auto-correlation for long-term series forecasting,\u201d in\nNeural Information Processing Systems, 2021."},{"key":"ref20","unstructured":"T. Zhou, Z. Ma, Q. Wen, X. Wang, L. Sun, and R. Jin, \u201cFEDformer:\nFrequency enhanced decomposed transformer for long-term series\nforecasting,\u201d in Proceedings of the 39th International Conference on\nMachine Learning, ser. Proceedings of Machine Learning Research.\nPMLR, 2022."},{"key":"ref21","unstructured":"X. Zhang, X. Jin, K. Gopalswamy, G. Gupta, Y. Park, X. Shi, H. Wang,\nD. C. Maddix, and Y. Wang, \u201cFirst de-trend then attend: Rethinking attention for time-series forecasting,\u201d arXiv preprint https:\/\/arxiv.org\/abs\/2212.08151,\n2022."},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"G. E. P. Box and D. A. Pierce, \u201cDistribution of residual\nautocorrelations in autoregressive-integrated moving average time\nseries models,\u201d Journal of the American Statistical Association,\nvol. 65, no. 332, pp. 1509\u20131526, 1970. [Online]. Available: https:\/\/www.tandfonline.com\/doi\/abs\/10.1080\/01621459.1970.10481180","DOI":"10.1080\/01621459.1970.10481180"},{"key":"ref23","unstructured":"G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecasting\nand Control, 3rd ed. USA: Prentice Hall PTR, 1994."},{"key":"ref24","unstructured":"H.-F. Yu, N. Rao, and I. S. Dhillon, \u201cTemporal regularized matrix factorization for high-dimensional time series prediction,\u201d in Proceedings\nof the 30th International Conference on Neural Information Processing\nSystems, ser. NIPS\u201916. Red Hook, NY, USA: Curran Associates Inc.,\n2016, p. 847\u2013855."},{"key":"ref25","doi-asserted-by":"publisher","unstructured":"P. R. Winters, Forecasting Sales by Exponentially Weighted\nMoving Averages. Berlin, Heidelberg: Springer Berlin Heidelberg,\n1976, pp. 384\u2013386. [Online]. Available: https:\/\/doi.org\/10.1007\/978-3-642-51565-1_116","DOI":"10.1007\/978-3-642-51565-1_116"},{"key":"ref26","doi-asserted-by":"crossref","unstructured":"C. C. Holt, \u201cForecasting seasonals and trends by exponentially\nweighted moving averages,\u201d International Journal of Forecasting,\nvol. 20, no. 1, pp. 5\u201310, 2004. [Online]. Available: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0169207003001134","DOI":"10.1016\/j.ijforecast.2003.09.015"},{"key":"ref27","unstructured":"I. Melnyk and A. Banerjee, \u201cEstimating structured vector\nautoregressive models,\u201d in Proceedings of The 33rd International\nConference on Machine Learning, ser. Proceedings of Machine\nLearning Research, M. F. Balcan and K. Q. Weinberger, Eds., vol. 48.\nNew York, New York, USA: PMLR, 20\u201322 Jun 2016, pp. 830\u2013839.\n[Online]. Available: http:\/\/proceedings.mlr.press\/v48\/melnyk16.html"},{"key":"ref28","unstructured":"H. Qiu, S. Xu, F. Han, H. Liu, and B. Caffo, \u201cRobust estimation\nof transition matrices in high dimensional heavy-tailed vector\nautoregressive processes,\u201d in Proceedings of the 32nd International\nConference on Machine Learning, ser. Proceedings of Machine\nLearning Research, F. Bach and D. Blei, Eds., vol. 37. Lille,\nFrance: PMLR, 07\u201309 Jul 2015, pp. 1843\u20131851. [Online]. Available:\nhttp:\/\/proceedings.mlr.press\/v37\/qiu15.html"},{"key":"ref29","unstructured":"R. Frigola, F. Lindsten, T. B. Sch\u00f6n, and C. E. Rasmussen, \u201cBayesian\ninference and learning in Gaussian process state-space models with\nparticle MCMC,\u201d https:\/\/arxiv.org\/abs\/1306.2861 [stat.ML], 2013."},{"key":"ref30","doi-asserted-by":"crossref","unstructured":"J. L. Elman, \u201cFinding structure in time,\u201d Cognitive Science,\nvol. 14, no. 2, pp. 179\u2013211, 1990. [Online]. Available: https:\/\/www.sciencedirect.com\/science\/article\/pii\/036402139090002E","DOI":"10.1016\/0364-0213(90)90002-E"},{"key":"ref31","unstructured":"J. Connor, L. Atlas, and D. Martin, \u201cRecurrent networks and narma\nmodeling,\u201d in Advances in Neural Information Processing Systems,\nJ. Moody, S. Hanson, and R. P. Lippmann, Eds., vol. 4. Morgan-Kaufmann, 1992. [Online]. Available: https:\/\/proceedings.neurips.cc\/paper\/1991\/file\/5ef0b4eba35ab2d6180b0bca7e46b6f9-Paper.pdf"},{"key":"ref32","unstructured":"T. Mikolov, A. Joulin, S. Chopra, M. Mathieu, and M. Ranzato,\n\u201cLearning longer memory in recurrent neural networks,\u201d in Workshop\nat the International Conference on Learning Representation; ICLR\n2015, 12 2015."},{"key":"ref33","doi-asserted-by":"publisher","unstructured":"Y. Bengio, P. Simard, and P. Frasconi, \u201cLearning long-term\ndependencies with gradient descent is difficult,\u201d Trans. Neur.\nNetw., vol. 5, no. 2, p. 157\u2013166, Mar. 1994. [Online]. Available:\nhttps:\/\/doi.org\/10.1109\/72.279181","DOI":"10.1109\/72.279181"},{"key":"ref34","doi-asserted-by":"publisher","unstructured":"S. Hochreiter, \u201cThe vanishing gradient problem during learning\nrecurrent neural nets and problem solutions,\u201d Int. J. Uncertain.\nFuzziness Knowl.-Based Syst., vol. 6, no. 2, p. 107\u2013116, Apr. 1998.\n[Online]. Available: https:\/\/doi.org\/10.1142\/S0218488598000094","DOI":"10.1142\/S0218488598000094"},{"key":"ref35","unstructured":"N. Srivastava, E. Mansimov, and R. Salakhutdinov, \u201cUnsupervised\nlearning of video representations using lstms,\u201d in Proceedings of\nthe 32nd International Conference on International Conference on\nMachine Learning - Volume 37, ser. ICML\u201915. JMLR.org, 2015,\np. 843\u2013852."},{"key":"ref36","doi-asserted-by":"crossref","unstructured":"S. Ma, L. Sigal, and S. Sclaroff, \u201cLearning activity progression in lstms\nfor activity detection and early detection,\u201d in 2016 IEEE Conference\non Computer Vision and Pattern Recognition (CVPR), 2016, pp. 1942\u20131950.","DOI":"10.1109\/CVPR.2016.214"},{"key":"ref37","unstructured":"I. Sutskever, O. Vinyals, and Q. V. Le, \u201cSequence to sequence learning\nwith neural networks,\u201d in Proceedings of the 27th International\nConference on Neural Information Processing Systems - Volume 2, ser.\nNIPS\u201914. Cambridge, MA, USA: MIT Press, 2014, p. 3104\u20133112."},{"key":"ref38","doi-asserted-by":"crossref","unstructured":"K. Cho, B. van Merri\u00ebnboer, D. Bahdanau, and Y. Bengio,\n\u201cOn the properties of neural machine translation: Encoder\u2013decoder\napproaches,\u201d in Proceedings of SSST-8, Eighth Workshop on Syntax,\nSemantics and Structure in Statistical Translation. Doha, Qatar:\nAssociation for Computational Linguistics, Oct. 2014, pp. 103\u2013111.\n[Online]. Available: https:\/\/www.aclweb.org\/anthology\/W14-4012","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref39","doi-asserted-by":"publisher","unstructured":"O. Hamel and M. Fareh, \u201cEncoder-decoder neural network with\nattention mechanism for types detection in linked data,\u201d in\nProceedings of the 17th Conference on Computer Science and\nIntelligence Systems, ser. Annals of Computer Science and Information\nSystems, M. Ganzha, L. Maciaszek, M. Paprzycki, and D. \u015al\u0119zak,\nEds., vol. 30. IEEE, 2022, p. 733\u2013739. [Online]. Available:\nhttp:\/\/dx.doi.org\/10.15439\/2022F209","DOI":"10.15439\/2022F209"},{"key":"ref40","doi-asserted-by":"crossref","unstructured":"Y. Qin, D. Song, H. Cheng, W. Cheng, G. Jiang, and G. W. Cottrell,\n\u201cA dual-stage attention-based recurrent neural network for time series\nprediction,\u201d in Proceedings of the 26th International Joint Conference\non Artificial Intelligence, ser. IJCAI\u201917.\n AAAI Press, 2017, p.\n2627\u20132633.","DOI":"10.24963\/ijcai.2017\/366"},{"key":"ref41","doi-asserted-by":"publisher","unstructured":"S. Huang, D. Wang, X. Wu, and A. Tang, \u201cDSANet: Dual\nself-attention network for multivariate time series forecasting,\u201d in\nProceedings of the 28th ACM International Conference on Information\nand Knowledge Management, ser. CIKM \u201919. New York, NY, USA:\nAssociation for Computing Machinery, 2019, p. 2129\u20132132. [Online].\nAvailable: https:\/\/doi.org\/10.1145\/3357384.3358132","DOI":"10.1145\/3357384.3358132"},{"key":"ref42","doi-asserted-by":"publisher","unstructured":"T. Lin, T. Guo, and K. Aberer, \u201cHybrid neural networks for learning the\ntrend in time series,\u201d in Proceedings of the Twenty-Sixth International\nJoint Conference on Artificial Intelligence, IJCAI-17, 2017, pp. 2273\u20132279. [Online]. Available: https:\/\/doi.org\/10.24963\/ijcai.2017\/316","DOI":"10.24963\/ijcai.2017\/316"},{"key":"ref43","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, and J. Sun, \u201cDeep residual learning for\nimage recognition,\u201d 2016 IEEE Conference on Computer Vision and\nPattern Recognition (CVPR), pp. 770\u2013778, 2016.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref44","doi-asserted-by":"crossref","unstructured":"D. Salinas, V. Flunkert, J. Gasthaus, and T. Januschowski, \u201cDeepAR:\nProbabilistic forecasting with autoregressive recurrent networks,\u201d\nInternational Journal of Forecasting, vol. 36, no. 3, pp. 1181\u20131191,\n2020. [Online]. Available: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0169207019301888","DOI":"10.1016\/j.ijforecast.2019.07.001"},{"key":"ref45","doi-asserted-by":"crossref","unstructured":"Y. G. Cinar, H. Mirisaee, P. Goswami, E. Gaussier, A. A\u00eft-Bachir, and\nV. Strijov, \u201cPosition-based content attention for time series forecasting\nwith sequence-to-sequence rnns,\u201d in Neural Information Processing,\nD. Liu, S. Xie, Y. Li, D. Zhao, and E.-S. M. El-Alfy, Eds. Cham:\nSpringer International Publishing, 2017, pp. 533\u2013544.","DOI":"10.1007\/978-3-319-70139-4_54"},{"key":"ref46","unstructured":"R. Wen, K. Torkkola, B. Narayanaswamy, and D. Madeka, \u201cA multi-horizon quantile recurrent forecaster,\u201d https:\/\/arxiv.org\/abs\/ Machine Learning, 2017."},{"key":"ref47","doi-asserted-by":"publisher","unstructured":"I. M. Baytas, C. Xiao, X. Zhang, F. Wang, A. K. Jain, and J. Zhou,\n\u201cPatient subtyping via time-aware LSTM networks,\u201d in Proceedings\nof the 23rd ACM SIGKDD International Conference on Knowledge\nDiscovery and Data Mining, ser. KDD \u201917. New York, NY, USA:\nAssociation for Computing Machinery, 2017, p. 65\u201374. [Online].\nAvailable: https:\/\/doi.org\/10.1145\/3097983.3097997","DOI":"10.1145\/3097983.3097997"},{"key":"ref48","doi-asserted-by":"publisher","unstructured":"C. Fan, Y. Zhang, Y. Pan, X. Li, C. Zhang, R. Yuan, D. Wu,\nW. Wang, J. Pei, and H. Huang, \u201cMulti-horizon time series forecasting\nwith temporal attention learning,\u201d in Proceedings of the 25th ACM\nSIGKDD International Conference on Knowledge Discovery &\nData Mining, ser. KDD \u201919. New York, NY, USA: Association\nfor Computing Machinery, 2019, p. 2527\u20132535. [Online]. Available:\nhttps:\/\/doi.org\/10.1145\/3292500.3330662","DOI":"10.1145\/3292500.3330662"},{"key":"ref49","doi-asserted-by":"publisher","unstructured":"K. Kaczmarek, J. Pokrywka, and F. Grali\u0144ski, \u201cUsing transformer\nmodels for gender attribution in polish,\u201d in Proceedings of the\n17th Conference on Computer Science and Intelligence Systems, ser.\nAnnals of Computer Science and Information Systems, M. Ganzha,\nL. Maciaszek, M. Paprzycki, and D. \u015al\u02dbezak, Eds., vol. 30. IEEE, 2022,\np. 73\u201377. [Online]. Available: http:\/\/dx.doi.org\/10.15439\/2022F197","DOI":"10.15439\/2022F197"},{"key":"ref50","unstructured":"S. Li, X. Jin, Y. Xuan, X. Zhou, W. Chen, Y.-X. Wang,\nand X. Yan, \u201cEnhancing the locality and breaking the\nmemory bottleneck of transformer on time series forecasting,\u201d\nin Advances in Neural Information Processing Systems, H. Wallach,\nH. Larochelle, A. Beygelzimer, F. d'Alch\u00e9-Buc, E. Fox, and\nR. Garnett, Eds., vol. 32.\n Curran Associates, Inc., 2019.\n[Online]. Available: https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2019\/file\/6775a0635c302542da2c32aa19d86be0-Paper.pdf"},{"key":"ref51","doi-asserted-by":"crossref","unstructured":"H. Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong, and W. Zhang,\n\u201cInformer: Beyond efficient transformer for long sequence time-series\nforecasting,\u201d in AAAI Conference on Artificial Intelligence, 2020.","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"ref52","unstructured":"Y. Zhang and J. Yan, \u201cCrossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting,\u201d in\nInternational Conference on Learning Representation, 2023."},{"key":"ref53","doi-asserted-by":"crossref","unstructured":"F. M. Bianchi, E. Maiorino, M. Kampffmeyer, A. Rizzi, and\nR. Jenssen, Recurrent Neural Networks for Short-Term Load Forecasting: An Overview and Comparative Analysis. Springer, 01 2017.","DOI":"10.1007\/978-3-319-70338-1"},{"key":"ref54","unstructured":"D. P. Kingma and J. Ba, \u201cAdam: A method for stochastic\noptimization.\u201d Computing Research Repository (CoRR), vol.\nabs\/1412.6980, 2014. [Online]. Available: http:\/\/dblp.uni-trier.de\/db\/journals\/corr\/corr1412.html#KingmaB14"}],"event":{"name":"20th Conference on Computer Science and Intelligence Systems (FedCSIS)","theme":"Computer Science and Intelligence Systems","location":"Krak\u00f3w, Poland","acronym":"FedCSIS","number":"20","start":{"date-parts":[[2025,9,14]]},"end":{"date-parts":[[2025,9,17]]}},"container-title":["Annals of Computer Science and Information Systems","Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS)"],"original-title":[],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T07:46:46Z","timestamp":1761119206000},"score":1,"resource":{"primary":{"URL":"https:\/\/annals-csis.org\/Volume_43\/drp\/7041.html"}},"subtitle":[],"proceedings-subject":"Computer Science and Information Systems","short-title":[],"issued":{"date-parts":[[2025,10,15]]},"references-count":54,"URL":"https:\/\/doi.org\/10.15439\/2025f7041","relation":{},"ISSN":["2300-5963"],"issn-type":[{"value":"2300-5963","type":"print"}],"subject":[],"published":{"date-parts":[[2025,10,15]]}}}