{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T09:44:05Z","timestamp":1765619045228,"version":"3.48.0"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T00:00:00Z","timestamp":1756598400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T00:00:00Z","timestamp":1756598400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61572408"],"award-info":[{"award-number":["61572408"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s13042-025-02782-y","type":"journal-article","created":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T07:13:08Z","timestamp":1756624388000},"page":"10489-10504","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Patchlinear for single-step forecasting and anomaly detection"],"prefix":"10.1007","volume":"16","author":[{"given":"Min-Hua","family":"Zheng","sequence":"first","affiliation":[]},{"given":"Shi-Ying","family":"Lan","sequence":"additional","affiliation":[]},{"given":"Wan-Lei","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,31]]},"reference":[{"key":"2782_CR1","first-page":"481","volume-title":"Practical use cases and future trends of anomaly detection practical use cases and future trends of anomaly detection","author":"SK Adari","year":"2024","unstructured":"Adari SK, Alla S (2024) Practical use cases and future trends of anomaly detection practical use cases and future trends of anomaly detection. Springer, Berlin, pp 481\u2013509"},{"key":"2782_CR2","unstructured":"AIOpsChallenge (2017) KPI Anomaly detection competition (2017) KPI anomaly detection. URL http:\/\/iops.ai\/competition_detail\/?competition_id=5"},{"key":"2782_CR3","volume-title":"Outliers in statistical data outliers in statistical data (3)","author":"V Barnett","year":"1994","unstructured":"Barnett V, Lewis T (1994) Outliers in statistical data outliers in statistical data (3). Wiley, Hoboken"},{"key":"2782_CR4","unstructured":"Box GE, Jenkins GM (1976). Time series analysis: forecasting and control time series analysis: forecasting and control. Holden-Day, San Francisco"},{"key":"2782_CR5","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.neucom.2021.03.062","volume":"448","author":"R Chen","year":"2021","unstructured":"Chen R, Shi G, Zhao W, Liang C (2021) A joint model for IT operation series prediction and anomaly detection a joint model for IT operation series prediction and anomaly detection. Neurocomputing 448:130\u2013139. https:\/\/doi.org\/10.1016\/j.neucom.2021.03.062","journal-title":"Neurocomputing"},{"key":"2782_CR6","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1002\/int.20145","volume":"21","author":"S Chen","year":"2006","unstructured":"Chen S, Chung N (2006) Forecasting enrollments using high-order fuzzy time series and genetic algorithms. Int J Intell Syst 21:485\u2013501. https:\/\/doi.org\/10.1002\/int.20145","journal-title":"Int J Intell Syst"},{"key":"2782_CR7","doi-asserted-by":"crossref","unstructured":"Cho K, van Merri\u00ebnboer B, Gul\u00e7ehre \u00c7, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the EMNLP, pp 1724\u20131734, https:\/\/hal.science\/hal-01433235\/","DOI":"10.3115\/v1\/D14-1179"},{"key":"2782_CR8","doi-asserted-by":"publisher","unstructured":"Cirstea RG, Guo C, Yang B, Kieu T, Dong X, Pan S (2021) Triformer: triangular, variable-specific attentions for long sequence multivariate time series forecasting triformer: triangular, variable-specific attentions for long sequence multivariate time series forecasting. In: Proceedings of the IJCAI, pp 1\u201318, https:\/\/doi.org\/10.48550\/arXiv.2204.13767","DOI":"10.48550\/arXiv.2204.13767"},{"key":"2782_CR9","doi-asserted-by":"publisher","unstructured":"Connor JT, Martin RD, Atlas LE (1994) Recurrent neural networks and robust time series prediction recurrent neural networks and robust time series prediction. IEEE Trans Neural Netw 240\u2013254, https:\/\/doi.org\/10.1109\/72.279188","DOI":"10.1109\/72.279188"},{"key":"2782_CR10","doi-asserted-by":"publisher","unstructured":"Dang Y, Lin Q, Huang P (2019) AIOps: Real-world challenges and research innovations AIOps: real-world challenges and research innovations. In: Proceedings of the ICSE, pp 4\u20135, https:\/\/doi.org\/10.1109\/ICSE-Companion.2019.00023","DOI":"10.1109\/ICSE-Companion.2019.00023"},{"key":"2782_CR11","doi-asserted-by":"publisher","first-page":"5845","DOI":"10.1007\/s10489-022-03895-5","volume":"53","author":"A Golzari Oskouei","year":"2023","unstructured":"Golzari Oskouei A, Balafar MA, Motamed C (2023) EEDCWRN: efficient deep clustering with the weight of representations and the help of neighbors EDCWRN: efficient deep clustering with the weight of representations and the help of neighbors. Appl Intell 53:5845\u20135867. https:\/\/doi.org\/10.1007\/s10489-022-03895-5","journal-title":"Appl Intell"},{"key":"2782_CR12","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1016\/j.ijforecast.2006.01.001","volume":"22","author":"JGD Gooijer","year":"2006","unstructured":"Gooijer JGD, Hyndman RJ (2006) 25 years of time series forecasting. Int J Forecast 22:443\u2013473. https:\/\/doi.org\/10.1016\/j.ijforecast.2006.01.001","journal-title":"Int J Forecast"},{"key":"2782_CR13","doi-asserted-by":"publisher","unstructured":"Han Z, Zhao J, Leung H, Ma KF, Wang W (2021) A review of deep learning models for time series prediction a review of deep learning models for time series prediction. IEEE Sensors Journal 7833 \u2013 7848, https:\/\/doi.org\/10.1109\/JSEN.2019.2923982","DOI":"10.1109\/JSEN.2019.2923982"},{"key":"2782_CR14","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781107049994","volume-title":"Forecasting, structural time series models and the Kalman filter forecasting","author":"AC Harvey","year":"1990","unstructured":"Harvey AC (1990) Forecasting, Structural Time Series Models and the Kalman Filter Forecasting. Cambridge University Press, Cambridge"},{"key":"2782_CR15","unstructured":"Hawkular (2014). Hawkular for monitoring services: metrics, alerting, inventory, application performance management. https:\/\/github.com\/hawkular"},{"key":"2782_CR16","doi-asserted-by":"publisher","unstructured":"Hochreiter S, J\u00fcrgen S (1997). Long short-term memory. Neural Comput 1735\u20131780, https:\/\/doi.org\/10.1162\/neco.1997.9.8.17359","DOI":"10.1162\/neco.1997.9.8.17359"},{"key":"2782_CR17","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1038\/scientificamerican0792-66","volume":"267","author":"JH Holland","year":"1992","unstructured":"Holland JH (1992) Genetic algorithms. Sci Am 267:66\u201373 (http:\/\/www.jstor.org\/stable\/24939139)","journal-title":"Sci Am"},{"key":"2782_CR18","doi-asserted-by":"publisher","unstructured":"Hundman K, Constantinou V, Laporte C, Colwell I, Soderstrom T (2018) Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding. In: Proceedings of the SIGKDD, pp 387\u2013395, https:\/\/doi.org\/10.1145\/3219819.3219845","DOI":"10.1145\/3219819.3219845"},{"key":"2782_CR19","unstructured":"Kalekar PS (2004) Time series forecasting using holt-winters exponential smoothing. Kanwal Rekhi School of Information Technology, pp 1\u201313"},{"key":"2782_CR20","doi-asserted-by":"publisher","unstructured":"Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN, pp 1942\u20131948. https:\/\/doi.org\/10.1109\/ICNN.1995.488968","DOI":"10.1109\/ICNN.1995.488968"},{"key":"2782_CR21","unstructured":"Kim T, Kim J, Tae Y, Park C, Choi JH, Choo J (2021) Reversible instance normalization for accurate time-series forecasting against distribution shift. In: Proceedings of the ICLR. https:\/\/openreview.net\/forum?id=cGDAkQo1C0p"},{"key":"2782_CR22","doi-asserted-by":"publisher","unstructured":"Kingma DP, Welling M (2013). Auto-encoding variational bayes auto-encoding variational bayes. arXiv preprint arXiv:1312.6114. https:\/\/doi.org\/10.48550\/arXiv.1312.6114","DOI":"10.48550\/arXiv.1312.6114"},{"key":"2782_CR23","doi-asserted-by":"publisher","unstructured":"Lavin A, Ahmad S (2015) Evaluating real-time anomaly detection algorithms\u2013the numenta anomaly benchmark. In: Proceedings of the ICMLA, pp 38\u201344. https:\/\/doi.org\/10.1109\/ICMLA.2015.141","DOI":"10.1109\/ICMLA.2015.141"},{"key":"2782_CR24","unstructured":"LeCun Y (1987) Connexionist learning models connexionist learning models . ParisUniversite Pierre et Marie Curie"},{"key":"2782_CR25","doi-asserted-by":"crossref","unstructured":"Leys C, Ley C, Klein O, Bernard P, Licata L (2013) Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. J Exp Soc Psychol, pp 764\u2013766. https:\/\/doi.org\/10.1016\/j.jesp.2013.03.013","DOI":"10.1016\/j.jesp.2013.03.013"},{"key":"2782_CR26","doi-asserted-by":"publisher","unstructured":"Li D, Chen D, Jin B, Shi L, Goh J, Ng SK (2019) MAD-GAN: multivariate anomaly detection for time series data with generative adversarial networks. In: Proceedings of the ICANN, pp 703\u2013716. https:\/\/doi.org\/10.1007\/978-3-030-30490-4_56","DOI":"10.1007\/978-3-030-30490-4_56"},{"key":"2782_CR27","doi-asserted-by":"publisher","unstructured":"Li Z, Qi S, Li Y, Xu Z (2023) Revisiting long-term time series forecasting: an investigation on linear mapping. arXiv preprint arXiv:2305.10721. https:\/\/doi.org\/10.48550\/arXiv.2305.10721","DOI":"10.48550\/arXiv.2305.10721"},{"key":"2782_CR28","doi-asserted-by":"publisher","unstructured":"Liu S, Liu S, Yu H, Liao C, Li J, Lin W, Dustdar S (2022) Pyraformer: low-complexity pyramidal attention for long-range time series modeling and forecasting. Proceedings of ICLR, pp 1\u201312. https:\/\/doi.org\/10.34726\/2945","DOI":"10.34726\/2945"},{"key":"2782_CR29","unstructured":"Liu Y, Hu T, Zhang H, Wu H, Wang S, Ma L, Long M (2024) iTransformer: inverted transformers are effective for time series forecasting. In: Proceedings of the ICLR. https:\/\/openreview.net\/forum?id=JePfAI8fah"},{"key":"2782_CR30","first-page":"89","volume":"2015","author":"P Malhotra","year":"2015","unstructured":"Malhotra P, Vig L, Shroff G, Agarwal P (2015) Long short term memory networks for anomaly detection in time series. Esann, 89","journal-title":"Esann"},{"key":"2782_CR31","doi-asserted-by":"publisher","unstructured":"Mueen A, Keogh E, Zhu Q, Cash S, Westover B (2009) Exact discovery of time series motifs. In: Proceedings of the ICDM, pp 473-484. https:\/\/doi.org\/10.1137\/1.9781611972795.41","DOI":"10.1137\/1.9781611972795.41"},{"key":"2782_CR32","doi-asserted-by":"publisher","unstructured":"Nie Y, Nguyen NH, Sinthong P, Kalagnanam J (2023) A time series is worth 64 words: long-term forecasting with transformers. In: Proceedings of ICLR. https:\/\/doi.org\/10.48550\/arXiv.2211.14730","DOI":"10.48550\/arXiv.2211.14730"},{"key":"2782_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.simpa.2024.100678","volume":"21","author":"AG Oskouei","year":"2024","unstructured":"Oskouei AG, Samadi N, Tanha J, Bouyer A (2024) SSFCM-FWCW: semi-supervised fuzzy C-means method based on feature-weight and cluster-weight learning. Softw Impacts 21:100678. https:\/\/doi.org\/10.1016\/j.simpa.2024.100678","journal-title":"Softw Impacts"},{"key":"2782_CR34","doi-asserted-by":"publisher","first-page":"1544","DOI":"10.1109\/TMC.2019.2944829","volume":"3","author":"D Park","year":"2018","unstructured":"Park D, Hoshi Y, Kemp CC (2018) A multimodal anomaly detector for robot-assisted feeding using an LSTM-based variational autoencoder. IEEE Robot Autom Lett 3:1544\u20131551. https:\/\/doi.org\/10.1109\/TMC.2019.2944829","journal-title":"IEEE Robot Autom Lett"},{"key":"2782_CR35","doi-asserted-by":"publisher","unstructured":"Qin Y, Song D, Cheng H, Cheng W, Jiang G, Cottrell GW (2017) A dual-stage attention-based recurrent neural network for time series prediction. In: Proceedings of the IJCAI, pp 2627\u20132633. https:\/\/doi.org\/10.24963\/ijcai.2017\/366","DOI":"10.24963\/ijcai.2017\/366"},{"key":"2782_CR36","doi-asserted-by":"publisher","unstructured":"Ren H, Zhang Q, Xu B, Wang Y, Yi C, Huang C, Tong J (2019) Time-series anomaly detection service at microsoft. In: Proceedings of the SIGKDD, pp 3009\u20133017. https:\/\/doi.org\/10.1145\/3292500.3330680","DOI":"10.1145\/3292500.3330680"},{"key":"2782_CR37","unstructured":"Ruff L, Vandermeulen R, Goernitz N, Deecke L, Siddiqui SA, Binder A, Kloft M (2018) Deep one-class classification. In: Proceedings of the ICML, pp 4393\u20134402. https:\/\/proceedings.mlr.press\/v80\/ruff18a.html"},{"key":"2782_CR38","doi-asserted-by":"publisher","unstructured":"Siffer A, Fouque PA, Termier A, Largouet C (2017) Anomaly detection in streams with extreme value theory. In: Proceedings of the SIGKDD, pp 1067\u20131075. https:\/\/doi.org\/10.1145\/3097983.3098144","DOI":"10.1145\/3097983.3098144"},{"key":"2782_CR39","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1007\/s13042-015-0332-y","volume":"8","author":"P Singh","year":"2017","unstructured":"Singh P (2017) A brief review of modeling approaches based on fuzzy time series. Int J Mach Learn Cybern 8:397\u2013420. https:\/\/doi.org\/10.1007\/s13042-015-0332-y","journal-title":"Int J Mach Learn Cybern"},{"key":"2782_CR40","doi-asserted-by":"publisher","first-page":"1643","DOI":"10.1007\/s13042-020-01064-z","volume":"11","author":"P Singh","year":"2020","unstructured":"Singh P (2020) A novel hybrid time series forecasting model based on neutrosophic-PSO approach. Int J Mach Learn Cybern 11:1643\u20131658. https:\/\/doi.org\/10.1007\/s13042-020-01064-z","journal-title":"Int J Mach Learn Cybern"},{"key":"2782_CR41","doi-asserted-by":"publisher","unstructured":"Song H, Rajan D, Thiagarajan J, Spanias A (2018) Attend and diagnose: clinical time series analysis using attention models. In: Proceedings of the AAAI, pp 4091\u20134098. https:\/\/doi.org\/10.1609\/aaai.v32i1.11635","DOI":"10.1609\/aaai.v32i1.11635"},{"key":"2782_CR42","doi-asserted-by":"publisher","unstructured":"Su Y, Zhao Y, Niu C, Liu R, Sun W, Pei D (2019) Robust anomaly detection for multivariate time series through stochastic recurrent neural network. In: Proceedings of the SIGKDD, pp 2828\u20132837. https:\/\/doi.org\/10.1145\/3292500.3330672","DOI":"10.1145\/3292500.3330672"},{"key":"2782_CR43","doi-asserted-by":"publisher","unstructured":"Taylor SJ, Letham B (2018) Forecasting at scale. Am Stat, pp 37\u201345. https:\/\/doi.org\/10.1080\/00031305.2017.1380080","DOI":"10.1080\/00031305.2017.1380080"},{"key":"2782_CR44","doi-asserted-by":"crossref","unstructured":"Tuli S, Casale G, Jennings NR (2022) TranAD: deep transformer networks for anomaly detection in multivariate time series data. In: Proceedings of the VLDB endowment, vol 15, pp 1201\u20131214, https:\/\/doi.org\/10.14778\/3514061.3514067","DOI":"10.14778\/3514061.3514067"},{"key":"2782_CR45","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Polosukhin I (2017) Attention is all you need. Advances in neural information processing systems, pp 6000\u20136010. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/file\/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf"},{"key":"2782_CR46","doi-asserted-by":"crossref","unstructured":"Wang Z, Pei C, Ma M, Wang X, Li Z, Pei D et al (2024) Revisiting VAE for unsupervised time series anomaly detection: a frequency perspective. In: Proceedings of the WWW DOI 10(1145\/3589334):36457109","DOI":"10.1145\/3589334.3645710"},{"key":"2782_CR47","doi-asserted-by":"publisher","unstructured":"Widmer G, Kubat M (1996) Learning in the presence of concept drift and hidden contexts. Mach Learn, pp 69\u2013101. https:\/\/doi.org\/10.1023\/A:1018046501280","DOI":"10.1023\/A:1018046501280"},{"key":"2782_CR48","doi-asserted-by":"publisher","unstructured":"Wu, H. , Xu, J. , Wang, J. Long, M. 2021. Autoformer: decomposition transformers with auto-correlation for long-term series forecasting. Advances in neural information processing systems, pp 22419\u201322430. https:\/\/doi.org\/10.5555\/3540261.3541978","DOI":"10.5555\/3540261.3541978"},{"key":"2782_CR49","doi-asserted-by":"publisher","unstructured":"Xu H, Chen W, Zhao N, Li Z, Bu J, Li Z, others (2018) Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications. In: Proceedings of the WWW, pp 187\u2013196. https:\/\/doi.org\/10.1145\/3178876.3185996","DOI":"10.1145\/3178876.3185996"},{"key":"2782_CR50","unstructured":"Xu J, Wu H, Wang J, Long M (2022) Anomaly transformer: time series anomaly detection with association discrepancy. In: Proceedings of the ICLR. https:\/\/openreview.net\/forum?id=LzQQ89U1qm_"},{"key":"2782_CR51","doi-asserted-by":"publisher","unstructured":"Xue J, Yan F, Chen LY, Scherer T, Smirni E (2015) PRACTISE: robust prediction of data center time series PRACTISE: robust prediction of data center time series. In: Proceedings of the CNSM, pp 126\u2013134. https:\/\/doi.org\/10.1109\/CNSM.2015.7367348","DOI":"10.1109\/CNSM.2015.7367348"},{"key":"2782_CR52","unstructured":"YahooLabs S (2015) A labeled anomaly detection dataset, version 1.0 (2015). A labeled anomaly detection dataset, version 1.0 (2015). https:\/\/webscope.sandbox.yahoo.com\/catalog.php?datatype=s%26did=70"},{"key":"2782_CR53","doi-asserted-by":"publisher","unstructured":"Yeh C CM, Zhu Y, Ulanova L, Begum N, Ding Y, Dau HA, Keogh E (2016) Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and Shapelets matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and Shapelets. In: Proceedings of the ICDM, pp 1317\u20131322. https:\/\/doi.org\/10.1109\/ICDM.2016.0179","DOI":"10.1109\/ICDM.2016.0179"},{"key":"2782_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2016.12.032","author":"A Zameer","year":"2017","unstructured":"Zameer A, Arshad J, Khan A, Raja MAZ (2017) Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks. Energy Convers Manage. https:\/\/doi.org\/10.1016\/j.enconman.2016.12.032","journal-title":"Energy Convers Manage"},{"key":"2782_CR55","doi-asserted-by":"publisher","unstructured":"Zeng A, Chen M, Zhang L, Xu Q (2023) Are transformers effective for time series forecasting? Are transformers effective for time series forecasting?. In: Proceedings of the AAAI, pp 11121\u201311128. https:\/\/doi.org\/10.1609\/aaai.v37i9.26317","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"2782_CR56","doi-asserted-by":"publisher","unstructured":"Zhang C, Zhou T, Wen Q, Sun L (2022) TFAD: a decomposition time series anomaly detection architecture with time-frequency analysis TFAD: a decomposition time series anomaly detection architecture with time-frequency analysis. In: Proceedings of the CIKM, pp 2497\u20132507. https:\/\/doi.org\/10.1145\/3511808.3557470","DOI":"10.1145\/3511808.3557470"},{"key":"2782_CR57","doi-asserted-by":"publisher","unstructured":"Zhang Z, Li W, Ding W, Zhang L, Lu Q, Hu P, Lu S (2023) STAD-GAN: unsupervised anomaly detection on multivariate time series with self-training generative adversarial networks STAD-GAN: unsupervised anomaly detection on multivariate time series with self-training generative adversarial networks. ACM transactions on knowledge discovery from data, pp 1\u201318. https:\/\/doi.org\/10.1145\/35727809","DOI":"10.1145\/35727809"},{"key":"2782_CR58","doi-asserted-by":"crossref","unstructured":"Zhou B, Liu S, Hooi B, Cheng X, Ye J (2019) BeatGAN: anomalous rhythm detection using adversarially generated time series. BeatGAN: anomalous rhythm detection using adversarially generated time series. In: Proceedings of the IJCAI, pp 4433\u20134439. https:\/\/dl.acm.org\/doi\/abs\/10.5555\/3367471.3367658","DOI":"10.24963\/ijcai.2019\/616"},{"key":"2782_CR59","doi-asserted-by":"publisher","unstructured":"Zhou H, Zhang S, Peng J, Zhang S, Li J, Xiong H, Zhang W (2021). Informer: beyond efficient transformer for long sequence time-series forecasting informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI, pp 11106\u201311115. https:\/\/doi.org\/10.1609\/aaai.v35i12.17325","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"2782_CR60","unstructured":"Zhou T, Ma Z, Wen Q, Wang X, Sun L, Jin R (2022) FEDformer: frequency enhanced decomposed transformer for long-term series forecasting FEDformer: frequency enhanced decomposed transformer for long-term series forecasting. In: Proceedings of ICML, pp 1\u201319. https:\/\/proceedings.mlr.press\/v162\/zhou22g.html"},{"key":"2782_CR61","doi-asserted-by":"publisher","unstructured":"Zhu Y, Zimmerman Z, Senobari NS, Yeh CCM, Funning G, Mueen A, Keogh E (2016) Matrix Profile II: exploiting a novel algorithm and GPUs to break the one hundred million barrier for time series motifs and joins matrix profile II: exploiting a novel algorithm and GPUs to break the one hundred million barrier for time series motifs and joins. In: Proceedings of the ICDM, pp 739\u2013748. https:\/\/doi.org\/10.1109\/ICDM.2016.0085","DOI":"10.1109\/ICDM.2016.0085"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02782-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02782-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02782-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T09:41:08Z","timestamp":1765618868000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02782-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,31]]},"references-count":61,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["2782"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02782-y","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2025,8,31]]},"assertion":[{"value":"12 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}