{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T14:51:23Z","timestamp":1772981483570,"version":"3.50.1"},"reference-count":42,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/http\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/http\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001792","name":"James Cook University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001792","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Time\u2010series forecasting is essential for predicting events in the future and for tracking objects. The conventional recurrent neural network model needs to pad the target with zeros when handling long inputs, resulting in a loss in accuracy. Recently, it was proposed to divide a time series input into patches and merge the learned weights. However, such a model is difficult to interpret. In this article, we consider a mixture of continuous and discrete Markov states to model long\u2010range time dependencies. For example, in a vehicle, each gear level can be a discrete state and the throttle input is continuously controlled to maximise the efficiency of the engine. Data collected from the sensor is prone to noise due to component faults or external disturbances. Hence, we apply a stability constraint to select samples for training. We validate our algorithm on three datasets: (1) Apple Watch, (2) Car engine and (3) Election tweets. On all datasets, we achieve an improvement in the range of 5%\u201320% in the F\u2010measure. Furthermore, the features learned are easy to explain in terms of real\u2010world scenarios.<\/jats:p>","DOI":"10.1111\/exsy.70144","type":"journal-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T08:51:18Z","timestamp":1758617478000},"update-policy":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Continuous Time Markov Chain for Smartwatch Sensors"],"prefix":"10.1111","volume":"42","author":[{"ORCID":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/orcid.org\/0000-0003-4602-2080","authenticated-orcid":false,"given":"Iti","family":"Chaturvedi","sequence":"first","affiliation":[{"name":"College of Science and Engineering James Cook University  Townsville Queensland Australia"}]},{"ORCID":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/orcid.org\/0009-0006-2074-3671","authenticated-orcid":false,"given":"Wei Liang","family":"Seow","sequence":"additional","affiliation":[{"name":"College of Computing and Data Science Nanyang Technological University  Singapore"}]},{"given":"Amber","family":"Hogarth","sequence":"additional","affiliation":[{"name":"College of Science and Engineering James Cook University  Townsville Queensland Australia"}]},{"given":"Luca","family":"Adornetto","sequence":"additional","affiliation":[{"name":"College of Science and Engineering James Cook University  Townsville Queensland Australia"}]},{"given":"Erik","family":"Cambria","sequence":"additional","affiliation":[{"name":"College of Computing and Data Science Nanyang Technological University  Singapore"}]}],"member":"311","published-online":{"date-parts":[[2025,9,23]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2023.3254179"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.02.120"},{"key":"e_1_2_9_4_1","first-page":"108","volume-title":"Sentic Blending: Scalable Multimodal Fusion for Continuous Interpretation of Semantics and Sentics","author":"Cambria E.","year":"2013"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICOSP.2010.5657072"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-04391-8_33"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.103111"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1201\/b16014-19"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2013.45"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2013.68"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2014.07.002"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2016.07.019"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-024-10344-7"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.13589"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.08.127"},{"key":"e_1_2_9_16_1","first-page":"4171","volume-title":"BERT: Pre\u2010Training of Deep Bidirectional Transformers for Language Understanding","author":"Devlin J.","year":"2019"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-021-10063-7"},{"issue":"4","key":"e_1_2_9_18_1","doi-asserted-by":"crossref","DOI":"10.1136\/bmjsem-2020-001004","article-title":"Predicting Lying, Sitting, Walking and Running Using Apple Watch and Fitbit Data","volume":"7","author":"Fuller D.","year":"2021","journal-title":"BMJ Open Sport & Exercise Medicine"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-011-9101-8"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.21474\/IJAR01\/15280"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/24.61316"},{"key":"e_1_2_9_22_1","first-page":"80","volume-title":"Proceedings of the 2020 Conference on Robot Learning (CoRL 2020)","author":"Jena R.","year":"2021"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/9.57016"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i11.33303"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.12.057"},{"key":"e_1_2_9_26_1","first-page":"29464","article-title":"C\u2010GAIL: Stabilizing Generative Adversarial Imitation Learning With Control Theory","volume":"38","author":"Luo T.","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_9_27_1","first-page":"1184","volume-title":"Bottom\u2010Up and Top\u2010Down: Predicting Personality With Psycholinguistic and Language Model Features","author":"Mehta Y.","year":"2020"},{"key":"e_1_2_9_28_1","first-page":"3481","volume-title":"Proceedings of the 35th International Conference on Machine Learning (ICML)","author":"Mescheder L.","year":"2018"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2016.2572540"},{"key":"e_1_2_9_30_1","first-page":"2960","volume-title":"Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS 2012)","author":"Snoek J.","year":"2012"},{"key":"e_1_2_9_31_1","unstructured":"Spaeh F. C. andC.Tsourakakis.2024.\u201cLearning Mixtures of Continuous\u2010Time Markov Chains.\u201dInThe Web Conference 2024."},{"key":"e_1_2_9_32_1","first-page":"26313","article-title":"Minimax Optimal Online Imitation Learning via Replay Estimation","volume":"35","author":"Swamy G.","year":"2022","journal-title":"Advances in Neural Information Processing Systems (NeurIPS 2022)"},{"key":"e_1_2_9_33_1","doi-asserted-by":"publisher","DOI":"10.3390\/bdcc7030136"},{"key":"e_1_2_9_34_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0292310"},{"key":"e_1_2_9_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00034-019-01116-y"},{"key":"e_1_2_9_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-019-08328-z"},{"key":"e_1_2_9_37_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0218488520500294"},{"key":"e_1_2_9_38_1","volume-title":"ICLR 2023","author":"Yao S.","year":"2023"},{"key":"e_1_2_9_39_1","first-page":"5364","volume-title":"GLiNER: Generalist Model for Named Entity Recognition Using Bidirectional Transformer","author":"Zaratiana U.","year":"2024"},{"key":"e_1_2_9_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11768-011-0234-9"},{"key":"e_1_2_9_41_1","unstructured":"Zhenwei D. C.Luo Z.Li et\u00a0al.2024.\u201cRA\u2010NER: Retrieval Augmented NER for Knowledge Intensive Named Entity Recognition \u201dThe Second Tiny Papers Track at ICLR 2024."},{"key":"e_1_2_9_42_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"e_1_2_9_43_1","first-page":"2251","volume-title":"MELM: Data Augmentation With Masked Entity Language Modeling for Low\u2010Resource NER","author":"Zhou R.","year":"2022"}],"container-title":["Expert Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/exsy.70144","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/exsy.70144","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/exsy.70144","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T08:21:16Z","timestamp":1772958076000},"score":1,"resource":{"primary":{"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/onlinelibrary.wiley.com\/doi\/10.1111\/exsy.70144"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,23]]},"references-count":42,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["10.1111\/exsy.70144"],"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1111\/exsy.70144","archive":["Portico"],"relation":{},"ISSN":["0266-4720","1468-0394"],"issn-type":[{"value":"0266-4720","type":"print"},{"value":"1468-0394","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,23]]},"assertion":[{"value":"2025-07-22","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-11","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70144"}}