{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T05:37:27Z","timestamp":1782365847025,"version":"3.54.5"},"reference-count":145,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T00:00:00Z","timestamp":1691971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The multi-layer structures of Deep Learning facilitate the processing of higher-level abstractions from data, thus leading to improved generalization and widespread applications in diverse domains with various types of data. Each domain and data type presents its own set of challenges. Real-world time series data may have a non-stationary data distribution that may lead to Deep Learning models facing the problem of catastrophic forgetting, with the abrupt loss of previously learned knowledge. Continual learning is a paradigm of machine learning to handle situations when the stationarity of the datasets may no longer be true or required. This paper presents a systematic review of the recent Deep Learning applications of sensor time series, the need for advanced preprocessing techniques for some sensor environments, as well as the summaries of how to deploy Deep Learning in time series modeling while alleviating catastrophic forgetting with continual learning methods. The selected case studies cover a wide collection of various sensor time series applications and can illustrate how to deploy tailor-made Deep Learning, advanced preprocessing techniques, and continual learning algorithms from practical, real-world application aspects.<\/jats:p>","DOI":"10.3390\/s23167167","type":"journal-article","created":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T11:07:10Z","timestamp":1692011230000},"page":"7167","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Continual Deep Learning for Time Series Modeling"],"prefix":"10.3390","volume":"23","author":[{"given":"Sio-Iong","family":"Ao","sequence":"first","affiliation":[{"name":"International Association of Engineers, Unit 1, 1\/F, Hung To Road, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haytham","family":"Fayek","sequence":"additional","affiliation":[{"name":"School of Computing Technologies, RMIT University, Building 14, Melbourne, VIC 3000, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105963","DOI":"10.1016\/j.asoc.2019.105963","article-title":"A Spatial-Temporal Decomposition Based Deep Neural Network for Time Series Forecasting","volume":"87","author":"Asadi","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ao, S.I. (2010). Applied Time Series Analysis and Innovative Computing, Springer.","DOI":"10.1007\/978-90-481-8768-3"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.future.2017.09.082","article-title":"Efficient IoT-Based Sensor Big Data Collection-Processing and Analysis in Smart Buildings","volume":"82","author":"Plageras","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ao, S.I., Gelman, L., Karimi, H.R., and Tiboni, M. (2022). Advances in Machine Learning for Sensing and Condition Monitoring. Appl. Sci., 12.","DOI":"10.3390\/app122312392"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ao, S.I. (2008). Data Mining and Applications in Genomics, Springer.","DOI":"10.1007\/978-1-4020-8975-6"},{"key":"ref_6","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep Learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zheng, X.C., Wang, M., and Ordieres-Mer\u00e9, J. (2018). Comparison of Data Preprocessing Approaches for Applying Deep Learning to Human Activity Recognition in the Context of Industry 4.0. Sensors, 18.","DOI":"10.3390\/s18072146"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e2207720119","DOI":"10.1073\/pnas.2207720119","article-title":"Deep Learning for Tipping Points: Preprocessing Matters","volume":"119","author":"Dablander","year":"2022","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"17149","DOI":"10.1007\/s00521-020-05169-y","article-title":"A Novel Validation Framework to Enhance Deep Learning Models in Time-Series Forecasting","volume":"32","author":"Livieris","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_11","unstructured":"Guerrier, S., Molinari, R., Xu, H., and Zhang, Y. (2023, July 28). Applied Time Series Analysis with R. Available online: http:\/\/ts.smac-group.com."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Rhif, M., Abbes, A.B., Farah, I.R., Martinez, B., and Sang, Y. (2019). Wavelet Transform Application for\/in Non-Stationary Time-Series Analysis: A Review. Appl. Sci., 9.","DOI":"10.3390\/app9071345"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1007\/978-3-030-59338-4_19","article-title":"A Survey on Deep Learning for Time-Series Forecasting. Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges","volume":"77","author":"Mahmoud","year":"2021","journal-title":"Stud. Big Data"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/S1364-6613(99)01294-2","article-title":"Catastrophic Forgetting in Connectionist Networks","volume":"3","author":"French","year":"1999","journal-title":"Trends Cogn. Sci."},{"key":"ref_15","unstructured":"Lee, S., Goldt, S., and Saxe, A. (2021, January 18\u201324). Continual Learning in the Teacher-Student Setup: Impact of Task Similarity. Proceedings of the 38th International Conference on Machine Learning, PMLR, Virtual."},{"key":"ref_16","unstructured":"Shin, H., Lee, J.K., Kim, J., and Kim, J. (2017). Continual Learning with Deep Generative Replay. arXiv."},{"key":"ref_17","first-page":"3366","article-title":"A Continual Learning Survey: Defying Forgetting in Classification Tasks","volume":"44","author":"Aljundi","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","unstructured":"Wang, L., Zhang, X., Su, H., and Zhu, J. (2023). A Comprehensive Survey of Continual Learning: Theory, Method and Application. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1016\/j.tics.2020.09.004","article-title":"Embracing Change: Continual Learning in Deep Neural Networks","volume":"24","author":"Hadsell","year":"2020","journal-title":"Trends Cogn. Sci."},{"key":"ref_20","first-page":"2908","article-title":"Replay in Deep Learning: Current Approaches and Missing Biological Elements","volume":"33","author":"Hayes","year":"2021","journal-title":"Neural Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1016\/j.tins.2022.06.002","article-title":"Contributions by Metaplasticity to Solving the Catastrophic Forgetting Problem","volume":"45","author":"Jedlicka","year":"2022","journal-title":"Trends Neurosci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1038\/s42256-022-00452-0","article-title":"Biological Underpinnings for Lifelong Learning Machines","volume":"4","author":"Kudithipudi","year":"2022","journal-title":"Nat. Mach. Intell."},{"key":"ref_23","unstructured":"Kilickaya, M., Weijer, J.V., and Asano, Y. (2023). Towards Label-Efficient Incremental Learning: A survey. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.neucom.2021.10.021","article-title":"Online Continual Learning in Image Classification: An Empirical Survey","volume":"469","author":"Mai","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_25","unstructured":"Masana, M., Twardowski, B., and Weijer, J.V. (2020). On Class Orderings for Incremental Learning. arXiv."},{"key":"ref_26","unstructured":"Qu, H., Rahmani, H., Xu, L., Williams, B., and Liu, J. (2021). Recent Advances of Continual Learning in Computer Vision: An Overview. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Biesialska, M., Biesialska, K., and Costajussa, M.R. (2020, January 8\u201313). Continual Lifelong Learning in Natural Language Processing: A survey. Proceedings of the 28th International Conference on Computational Linguistics, Barcelona, Spain.","DOI":"10.18653\/v1\/2020.coling-main.574"},{"key":"ref_28","unstructured":"Ke, Z., and Liu, B. (2022). Continual Learning of Natural Language Processing Tasks: A Survey. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1613\/jair.1.13673","article-title":"Towards Continual Reinforcement Learning: A Review and Perspectives","volume":"75","author":"Khetarpal","year":"2022","journal-title":"J. Artif. Intell. Res."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Rajagukguk, R.A., Ramadhan, R.A.A., and Lee, H.J. (2020). A Review on Deep Learning Models for Forecasting Time Series Data of Solar Irradiance and Photovoltaic Power. Energies, 13.","DOI":"10.3390\/en13246623"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1078","DOI":"10.1016\/j.apenergy.2018.12.042","article-title":"Day-Ahead Building-Level Load Forecasts using Deep Learning vs. Traditional Time-Series Techniques","volume":"236","author":"Cai","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"120043","DOI":"10.1109\/ACCESS.2021.3107975","article-title":"Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines","volume":"9","author":"Choi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Deng, A., and Hooi, B. (2021, January 2\u20139). Graph Neural Network-Based Anomaly Detection in Multivariate Time Series. Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, Online.","DOI":"10.1609\/aaai.v35i5.16523"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1007\/s42452-020-2506-9","article-title":"Time Series Classification: Nearest Neighbor versus Deep Learning Models","volume":"2","author":"Jiang","year":"2020","journal-title":"SN Appl. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","article-title":"Deep Learning for Time Series Classification: A Review","volume":"33","author":"Forestier","year":"2019","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Han, T., and Sanchez-Azofeifa, G.A. (2022). A Deep Learning Time Series Approach for Leaf and Wood Classification from Terrestrial LiDAR Point Clouds. Remote Sens., 14.","DOI":"10.3390\/rs14133157"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"17188","DOI":"10.1038\/s41598-020-74215-5","article-title":"Understanding Deep Learning in Land Use Classification Based on Sentinel-2 Time Series","volume":"10","author":"Atzberger","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Naqvi, R.A., Arsalan, M., Rehman, A., Rehman, A.U., Loh, W.K., and Paul, A. (2020). Deep Learning-Based Drivers Emotion Classification System in Time Series Data for Remote Applications. Remote Sens., 12.","DOI":"10.3390\/rs12030587"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"82562","DOI":"10.1109\/ACCESS.2020.2990738","article-title":"Traffic Flow Forecast through Time Series Analysis Based on Deep Learning","volume":"8","author":"Zheng","year":"2020","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1109\/MCOM.2019.1800155","article-title":"Deep Learning with Long Short-Term Memory for Time Series Prediction","volume":"57","author":"Hua","year":"2019","journal-title":"IEEE Commun. Mag."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"105484","DOI":"10.1109\/ACCESS.2020.3000006","article-title":"Time Series Data for Equipment Reliability Analysis with Deep Learning","volume":"8","author":"Chen","year":"2020","journal-title":"IEEE Access"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"20200209","DOI":"10.1098\/rsta.2020.0209","article-title":"Time Series Forecasting with Deep Learning: A Survey","volume":"379","author":"Lim","year":"2021","journal-title":"Phil. Trans. R. Soc. A"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Yasrab, R., Zhang, J., Smyth, P., and Pound, M. (2021). Predicting Plant Growth from Time-Series Data Using Deep Learning. Remote Sens., 13.","DOI":"10.3390\/rs13030331"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.neucom.2020.05.087","article-title":"Multimodal Multitask Deep Learning Model for Alzheimer\u2019s Disease Progression Detection Based on Time Series Data","volume":"412","author":"Abuhmed","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2130001","DOI":"10.1142\/S0129065721300011","article-title":"An Experimental Review on Deep Learning Architectures for Time Series Forecasting","volume":"31","author":"Riquelme","year":"2021","journal-title":"Int. J. Neural Syst."},{"key":"ref_46","unstructured":"Cao, D., Wang, Y., Duan, J., Zhang, C., Zhu, X., Huang, C., Tong, Y., Xu, B., Bai, J., and Tong, J. (2020, January 6\u201312). Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting. Proceedings of the 34th Conference on Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_47","unstructured":"Torres, J.F., Troncoso, A., Koprinska, I., Wang, Z., and Mart\u00ednez-\u00c1lvarez, F. (2018, January 6\u20138). Deep Learning for Big Data Time Series Forecasting Applied to Solar Power. Proceedings of the International Joint Conference SOCO\u201918-CISIS\u201918-ICEUTE\u201918. SOCO\u201918-CISIS\u201918-ICEUTE\u201918 2018, San Sebasti\u00e1n, Spain. Advances in Intelligent Systems and Computing."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"104502","DOI":"10.1016\/j.envsoft.2019.104502","article-title":"A Spatiotemporal Deep Learning Model for Sea Surface Temperature Field Prediction using Time-series Satellite Data","volume":"120","author":"Xiao","year":"2019","journal-title":"Environ. Model. Softw."},{"key":"ref_49","unstructured":"Dau, H.A., Keogh, E., Kamgar, K., Yeh, C.C.M., Zhu, Y., Gharghabi, S., Ratanamahatana, C.A., Chen, Y., Hu, B., and Begum, N. (2023, May 20). The UCR Time Series Classification Archive. Available online: https:\/\/www.cs.ucr.edu\/~eamonn\/time_series_data_2018\/."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1126\/science.1091277","article-title":"Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication","volume":"304","author":"Jaeger","year":"2004","journal-title":"Science"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1007\/s10618-016-0483-9","article-title":"The Great Time Series Classification Bake off: A Review and Experimental Evaluation of Recent Algorithmic Advances","volume":"31","author":"Bagnall","year":"2017","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_52","unstructured":"Baydogan, M.G. (2019, February 28). Multivariate Time Series Classification Datasets. Available online: http:\/\/www.mustafabaydogan.com."},{"key":"ref_53","unstructured":"Borovykh, A., Bohte, S., and Oosterlee, C.W. (2017). Conditional Time Series Forecasting with Convolutional Neural Networks. arXiv."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1089\/big.2020.0159","article-title":"Deep Learning for Time Series Forecasting: A Survey","volume":"9","author":"Torres","year":"2021","journal-title":"Big Data"},{"key":"ref_55","unstructured":"Climate Commission (2013). The Critical Decade: Australia\u2019s Future\u2014Solar Energy."},{"key":"ref_56","unstructured":"Chollet, F., and Allaire, J. (2018). Deep Learning with R, Manning Publications."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"700","DOI":"10.1016\/j.apenergy.2018.12.004","article-title":"Assessment of deep recurrent neural network-based strategies for short-term building energy predictions","volume":"236","author":"Fan","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_58","first-page":"100204","article-title":"Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting","volume":"7","author":"Oyedele","year":"2022","journal-title":"Mach. Learn. Appl."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.ijforecast.2019.03.017","article-title":"A Hybrid Method of Exponential Smoothing and Recurrent Neural Networks for Time Series Forecasting","volume":"36","author":"Smyl","year":"2020","journal-title":"Int. J. Forecast."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1474","DOI":"10.1080\/15732479.2020.1815225","article-title":"Deep Learning-Based Detection of Structural Damage using Time-Series Data. Structure and Infrastructure Engineering","volume":"17","author":"Dang","year":"2021","journal-title":"Struct. Infrastruct. Eng."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014, January 14\u201321). Learning phrase representations using RNN encoder-decoder for statistical machine translation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Wang, F., Yu, Y., Zhang, Z., Li, J., Zhen, Z., and Li, K. (2018). Wavelet Decomposition and Convolutional LSTM Networks Based Improved Deep Learning Model for Solar Irradiance Forecasting. Appl. Sci., 8.","DOI":"10.3390\/app8081286"},{"key":"ref_64","first-page":"1951","article-title":"Deep Residual Learning for Image Recognition","volume":"45","author":"He","year":"2015","journal-title":"Indian J. Chem.-Sect. B Org. Med. Chem."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1145\/1111322.1111341","article-title":"Providing Public Intradomain Traffic Matrices to the Research Community","volume":"36","author":"Uhlig","year":"2006","journal-title":"SIGCOMM Comput. Commun. Rev."},{"key":"ref_66","first-page":"802","article-title":"Convolutional LSTM Network: A Machine Learning Approach for Precipitation nowcasting","volume":"28","author":"Shi","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","article-title":"Convolutional Neural Networks: An Overview and Application in Radiology","volume":"9","author":"Yamashita","year":"2018","journal-title":"Insights Imaging"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","article-title":"The Graph Neural Network Model","volume":"20","author":"Scarselli","year":"2009","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_69","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y. (2017). Graph Attention Networks. arXiv."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A Fast Learning Algorithm for Deep Belief Nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TII.2018.2793246","article-title":"Deep Coupling Autoencoder for Fault Diagnosis with Multimodal Sensory Data","volume":"14","author":"Ma","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"104706","DOI":"10.1016\/j.conengprac.2020.104706","article-title":"Processes soft modeling based on stacked autoencoders and wavelet extreme learning machine for aluminum plant-wide application","volume":"108","author":"Lei","year":"2021","journal-title":"Control Eng. Pract."},{"key":"ref_73","first-page":"1","article-title":"A Transformer Self Attention Model for Time Series Forecasting","volume":"9","author":"Pazouki","year":"2021","journal-title":"J. Electr. Comput. Eng. Innov."},{"key":"ref_74","unstructured":"Elsayed, S., Thyssens, D., Rashed, A., Jomaa, H.S., and Schmidt-Thieme, L. (2021). Do We Really Need Deep Learning Models for Time Series Forecasting?. arXiv."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1016\/j.procs.2020.04.056","article-title":"ECG Heartbeat Arrhythmia Classification Using Time-Series Augmented Signals and Deep Learning Approach","volume":"171","author":"Kanani","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Kisa, D.H., Ozdemir, M.A., Guren, O., and Akan, A. (2020, January 19\u201320). EMG based Hand Gesture Classification using Empirical Mode Decomposition Time-Series and Deep Learning. Proceedings of the 2020 Medical Technologies Congress, Antalya, Turkey.","DOI":"10.1109\/TIPTEKNO50054.2020.9299282"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Castro Filho, H.C., Carvalho Junior, O.A., Carvalho, O.L.F., Bem, P.P., Moura, R.S., Albuquerque, A.O., Silva, C.R., Ferreira, P.H.G., Guimaraes, R.F., and Gomes, R.A.T. (2020). Rice Crop Detection Using LSTM, Bi-LSTM, and Machine Learning Models from Sentinel-1 Time Series. Remote Sens., 12.","DOI":"10.3390\/rs12162655"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1016\/j.isprsjprs.2020.06.006","article-title":"Self-Attention for Raw Optical Satellite Time Series Classification","volume":"169","author":"RuBwurm","year":"2020","journal-title":"J. Photogramm. Remote Sens."},{"key":"ref_79","unstructured":"Kingphai, K., and Moshfeghi, Y. (2022). Ergonomics & Human Factors, CIEHF."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"57835","DOI":"10.1109\/ACCESS.2022.3178592","article-title":"Robust Unsupervised Anomaly Detection with Variational Autoencoder in Multivariate Time Series Data","volume":"10","author":"Yokkampon","year":"2022","journal-title":"IEEE Access"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.egyr.2019.11.009","article-title":"Comparison of Deep Learning Models for Multivariate Prediction of Time Series Wind Power Generation and Temperature","volume":"6","author":"Mishra","year":"2020","journal-title":"Energy Rep."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Wen, Q.S., Sun, L., Yang, F., Song, X., Gao, J., Wang, X., and Xu, H. (2021, January 19\u201327). Time Series Data Augmentation for Deep Learning: A Survey. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21), Montr\u00e9al, QC, Canada.","DOI":"10.24963\/ijcai.2021\/631"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.neucom.2020.02.097","article-title":"Robust IoT Time Series Classification with Data Compression and Deep Learning","volume":"398","author":"Azar","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and Dierentiation of Data by Simplified Least Squares Procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_85","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1111\/j.1469-8986.2010.01061.x","article-title":"Adjust: An Automatic EEG Artifact Detector Based on the Joint Use of Spatial and Temporal Features","volume":"48","author":"Mognon","year":"2011","journal-title":"Psychophysiology"},{"key":"ref_87","unstructured":"Kingphai, K., and Moshfeghi, Y. (2021). International Symposium on Human Mental Workload: Models and Applications, Springer."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Chalapathy, R., and Chawla, S. (2019). Deep Learning for Anomaly Detection: A Survey. arXiv.","DOI":"10.1145\/3394486.3406704"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1093\/bioinformatics\/btz470","article-title":"Scaling Tree-Based Automated Machine Learning to Biomedical Big Data with a Feature Set Selector","volume":"36","author":"Le","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_90","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural Machine Translation by Jointly Learning to Align and Translate. arXiv."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"14021","DOI":"10.1007\/s00521-021-06043-1","article-title":"Smoothing and Stationarity Enforcement Framework for Deep Learning Time-Series Forecasting","volume":"33","author":"Livieris","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"He, Y., Huang, Z., and Sick, B. (2021, January 18\u201322). Toward Application of Continuous Power Forecasts in a Regional Flexibility Market. Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China.","DOI":"10.1109\/IJCNN52387.2021.9533626"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"4221","DOI":"10.1038\/s41467-021-24483-0","article-title":"A Clinical Deep Learning Framework for Continually Learning from Cardiac Signals across Diseases, Time, Modalities, and Institutions","volume":"12","author":"Kiyasseh","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Gupta, V., Narwariya, J., Malhotra, P., Vig, L., and Shroff, G. (2021, January 7\u201310). Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions. Proceedings of the 2021 IEEE International Conference on Data Mining (ICDM), Auckland, New Zealand.","DOI":"10.1109\/ICDM51629.2021.00026"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"E10313","DOI":"10.1073\/pnas.1800755115","article-title":"Comparing Continual Task Learning in Minds and Machines","volume":"115","author":"Flesch","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/s10846-022-01603-6","article-title":"Continual Learning for Real-World Autonomous Systems: Algorithms, Challenges and Frameworks","volume":"105","author":"Shaheen","year":"2022","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_97","unstructured":"Pfulb, B., and Gepperth, A. (2019, January 6\u20139). A Comprehensive, Application-Oriented Study of Catastrophic Forgetting in DNNs. Proceedings of the International Conference on Learning Representations (ICLR), New Orleans, LA, USA."},{"key":"ref_98","unstructured":"Prabhu, A., Torr, P., and Dokania, P. (2020). European Conference on Computer Vision, Springer."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s42467-021-00009-8","article-title":"CLeaR: An Adaptive Continual Learning Framework for Regression Tasks","volume":"3","author":"He","year":"2021","journal-title":"AI Perspect."},{"key":"ref_100","unstructured":"Philps, D., Weyde, T., Garcez, A.D.A., and Batchelor, R. (2019). Continual Learning Augmented Investment Decisions. arXiv."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Chen, X., Wang, J., and Xie, K. (2021, January 19\u201327). TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learning. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, Montreal, QC, Canada.","DOI":"10.24963\/ijcai.2021\/498"},{"key":"ref_102","unstructured":"Lesort, T., George, T., and Rish, I. (2021). Continual learning in Deep Neural Networks: An Analysis of the Last Layer. arXiv."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.neunet.2023.01.014","article-title":"A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning","volume":"160","author":"Mundt","year":"2023","journal-title":"Neural Netw."},{"key":"ref_104","unstructured":"Bagus, B., Gepperth, A., and Lesort, T. (2022, January 5\u20137). Beyond Supervised Continual Learning: A Review. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium."},{"key":"ref_105","unstructured":"Pham, Q., Liu, C., Sahoo, D., and Hoi, S.C. (2022). Learning Fast and Slow for Online Time Series Forecasting. arXiv."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Xiao, Y., Liu, M., Zhang, Z., Jiang, L., Yin, M., and Wang, J. (2022). Streaming Traffic Flow Prediction Based on Continuous Reinforcement Learning. arXiv.","DOI":"10.1109\/ICDMW58026.2022.00011"},{"key":"ref_107","unstructured":"Sah, R.K., Mirzadeh, S.I., and Ghasemzadeh, H. (2022, January 11\u201315). Continual Learning for Activity Recognition. Proceedings of the 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Scotland, UK."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Matteoni, F., Cossu, A., Gallicchio, C., Lomonaco, V., and Bacciu, D. (2022, January 5\u20137). Continual Learning for Human State Monitoring. Proceedings of the ESANN 2022 Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium.","DOI":"10.14428\/esann\/2022.ES2022-38"},{"key":"ref_109","unstructured":"Kwon, Y.D., Chauhan, J., Kumar, A., Hui, P., and Mascolo, C. (2021, January 14\u201317). Exploring System Performance of Continual Learning for Mobile and Embedded Sensing Applications. Proceedings of the 2021 IEEE\/ACM Symposium on Edge Computing (SEC), San Jose, CA, USA."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1016\/j.neunet.2021.07.021","article-title":"Continual Learning for Recurrent Neural Networks: An Empirical Evaluation","volume":"143","author":"Cossu","year":"2021","journal-title":"Neural Netw."},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"He, Y. (2022). Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecasts. arXiv.","DOI":"10.21203\/rs.3.rs-251554\/v1"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Doshi, K., and Yilmaz, Y. (2022, January 4\u20138). Rethinking Video Anomaly Detection\u2014A Continual Learning Approach. Proceedings of the 2022 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA.","DOI":"10.1109\/WACV51458.2022.00309"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Maschler, B., Pham, T.T.H., and Weyrich, M. (2021, January 22\u201324). Regularization-based Continual Learning for Anomaly Detection in Discrete Manufacturing. Proceedings of the 54th CIRP Conference on Manufacturing Systems, Procedia CIRP, Virtual.","DOI":"10.1016\/j.procir.2021.11.076"},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Maschler, B., Vietz, H., Jazdi, N., and Weyrich, M. (2020, January 8\u201311). Continual Learning of Fault Prediction for Turbofan Engines using Deep Learning with Elastic Weight Consolidation. Proceedings of the 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation, Vienna, Austria.","DOI":"10.1109\/ETFA46521.2020.9211903"},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Bayram, B., and Ince, G. (2020). Real Time Auditory Scene Analysis using Continual Learning in Real Environments. Eur. J. Sci. Technol., 215\u2013226. Ejosat Special Issue 2020 (HORA).","DOI":"10.31590\/ejosat.779710"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s13218-020-00700-8","article-title":"Prediction Error-Driven Memory Consolidation for Continual Learning: On the Case of Adaptive Greenhouse Models","volume":"35","author":"Schillaci","year":"2021","journal-title":"KI\u2014K\u00fcnstliche Intell."},{"key":"ref_117","unstructured":"Knoblauch, J., Husain, H., and Diethe, T. (2020, January 13\u201318). Optimal Continual Learning has Perfect Memory and is NP-HARD. Proceedings of the 37th International Conference on Machine Learning, Virtual."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"3521","DOI":"10.1073\/pnas.1611835114","article-title":"Overcoming Catastrophic Forgetting in Neural Networks","volume":"114","author":"Kirkpatrick","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","article-title":"Learning without Forgetting","volume":"40","author":"Li","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Wei, H.R., Huang, S., Wang, R., Dai, X., and Chen, J. (2019, January 2\u20137). Online Distilling from Checkpoints for Neural Machine Translation. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, MN, USA.","DOI":"10.18653\/v1\/N19-1192"},{"key":"ref_121","unstructured":"Chaudhry, A., Ranzato, M., Rohrbach, M., and Elhoseiny, M. (May, January 30). Efficient Lifelong Learning with A-GEM. Proceedings of the International Conference on Learning Representations, Vancouver, Canada."},{"key":"ref_122","unstructured":"Chaudhry, A., Rohrbach, M., Elhoseiny, M., Ajanthan, T., Dokania, P.K., Torr, P.H.S., and Ranzato, M. (2019). On Tiny Episodic Memories in Continual Learning. arXiv."},{"key":"ref_123","unstructured":"Mirzadeh, S.I., Farajtabar, M., Gorur, D., Pascanu, R., and Ghasemzadeh, H. (2021, January 3\u20137). Linear Mode Connectivity in Multitask and Continual Learning. Proceedings of the ICLR 2021: The Ninth International Conference on Learning Representations, Virtual."},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Reiss, A., and Stricker, D. (2012, January 18\u201322). Introducing a New Benchmarked Dataset for Activity Monitoring. Proceedings of the 16th International Symposium on Wearable Computers, Newcastle UK.","DOI":"10.1109\/ISWC.2012.13"},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Schmidt, P., Reiss, A., Duerichen, R., Marberger, C., and Van Laerhoven, K. (2018, January 16\u201320). Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection. Proceedings of the 20th ACM International Conference on Multimodal Interaction, ICMI 2018, New York, NY, USA.","DOI":"10.1145\/3242969.3242985"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1109\/TAFFC.2016.2625250","article-title":"ASCERTAIN: Emotion and Personality Recognition using Commercial Sensors","volume":"9","author":"Subramanian","year":"2018","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_127","first-page":"4652","article-title":"Overcoming Catastrophic Forgetting by Incremental Moment Matching","volume":"Volume 30","author":"Lee","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1016\/j.procir.2022.09.091","article-title":"Regularization-Based Continual Learning for Fault Prediction in Lithium-Ion Batteries","volume":"112","author":"Maschler","year":"2022","journal-title":"Procedia CIRP"},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Eker, O., Camci, F., and Jennions, I. (2012, January 3\u20136). Major Challenges in Prognostics: Study on Benchmarking Prognostics Datasets. Proceedings of the 2012 1st European Conference of the Prognostics and Health Management Society, Dresden, Germany.","DOI":"10.36001\/phme.2012.v1i1.1409"},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Gonzalez, G.G., Casas, P., Fernandez, A., and Gomez, G. (2022, January 25\u201327). Steps towards Continual Learning in Multivariate Time-Series Anomaly Detection using Variational Autoencoders. Proceedings of the 22nd ACM Internet Measurement Conference, IMC \u201922, Nice, France.","DOI":"10.1145\/3517745.3563033"},{"key":"ref_131","unstructured":"Saxena, A., and Goebel, K. (2008). Turbofan Engine Degradation Simulation Data Set."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"524","DOI":"10.3389\/fgene.2019.00524","article-title":"Review of Causal Discovery Methods Based on Graphical Models","volume":"10","author":"Glymour","year":"2019","journal-title":"Front. Genet."},{"key":"ref_133","unstructured":"Castri, L., Mghames, S., and Bellotto, N. (2023). From Continual Learning to Causal Discovery in Robotics. arXiv."},{"key":"ref_134","first-page":"6467","article-title":"Gradient Episodic Memory for Continual Learning","volume":"30","author":"Ranzato","year":"2017","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"ref_135","first-page":"11849","article-title":"Online Continual Learning with Maximal Interfered Retrieval","volume":"32","author":"Aljundi","year":"2019","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-Level Control through Deep Reinforcement Learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"key":"ref_137","unstructured":"Bengio, E., Pineau, J., and Precup, D. (2020). Interference and Generalization in Temporal Difference Learning. arXiv."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.neucom.2020.11.050","article-title":"Pseudo-Rehearsal: Achieving Deep Reinforcement Learning without Catastrophic Forgetting","volume":"428","author":"Atkinson","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_139","unstructured":"Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., and Hadsell, R. (2016). Progressive Neural Networks. arXiv."},{"key":"ref_140","unstructured":"Yoon, J., Yang, E., Lee, J., and Hwang, S.J. (2017). Lifelong Learning with Dynamically Expandable Networks. arXiv."},{"key":"ref_141","unstructured":"He, Y., Huang, Z., and Sick, B. (March, January 28). Design of Explainability Module with Experts in the Loop for Visualization and Dynamic Adjustment of Continual Learning. Proceedings of the AAAI-22 Workshop on Interactive Machine Learning, Vancouver, Canada."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1007\/s10845-021-01793-0","article-title":"Continual Learning of Neural Networks for Quality Prediction in Production Using Memory Aware Synapses and Weight Transfer","volume":"33","author":"Tercan","year":"2022","journal-title":"J. Intell. Manuf."},{"key":"ref_143","unstructured":"Altun, K., and Barshan, B. (2010). International Workshop on Human Behavior Understanding, Springer."},{"key":"ref_144","unstructured":"Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J.L. (2012). International Workshop on Ambient Assisted Living, Springer."},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Saxena, A., Goebel, K., Simon, D., and Eklund, N. (2008, January 6\u20139). Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation. Proceedings of the 2008 International Conference on Prognostics and Health Management, Denver, CO, USA.","DOI":"10.1109\/PHM.2008.4711414"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/16\/7167\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:33:28Z","timestamp":1760128408000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/16\/7167"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,14]]},"references-count":145,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["s23167167"],"URL":"https:\/\/doi.org\/10.3390\/s23167167","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,14]]}}}