{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T12:11:54Z","timestamp":1773317514415,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T00:00:00Z","timestamp":1773187200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T00:00:00Z","timestamp":1773187200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100002301","name":"Estonian Research Council","doi-asserted-by":"crossref","award":["PRG1573"],"award-info":[{"award-number":["PRG1573"]}],"id":[{"id":"10.13039\/501100002301","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-026-08364-1","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T18:11:03Z","timestamp":1773252663000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Observability of a prediction model post-deployment data drift: the case of international trade value"],"prefix":"10.1007","volume":"82","author":[{"given":"Bassem","family":"Sellami","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chahinez","family":"Ounoughi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tarmo","family":"Kalvet","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marek","family":"Tiits","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sadok","family":"Ben Yahia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,11]]},"reference":[{"key":"8364_CR1","doi-asserted-by":"publisher","DOI":"10.1596\/978-1-4648-1457-0","volume-title":"World Development Report 2020: Trading for Development in the Age of Global Value Chains","author":"W Bank","year":"2020","unstructured":"Bank W (2020) World Development Report 2020: Trading for Development in the Age of Global Value Chains. World Bank, Washington. https:\/\/doi.org\/10.1596\/978-1-4648-1457-0"},{"key":"8364_CR2","doi-asserted-by":"publisher","DOI":"10.4337\/9781788113779","volume-title":"Handbook on Global Value Chains","year":"2019","unstructured":"Ponte S, Gereffi G, Raj-Reichert G (eds) (2019) Handbook on Global Value Chains. Edward Elgar Publishing, UK, Cheltenham. https:\/\/doi.org\/10.4337\/9781788113779"},{"key":"8364_CR3","doi-asserted-by":"publisher","DOI":"10.1596\/978-1-4648-0157-0","volume-title":"Making Global Value Chains Work for Development","author":"D Taglioni","year":"2016","unstructured":"Taglioni D, Winkler D (2016) Making Global Value Chains Work for Development. World Bank Publications, Washington DC. https:\/\/doi.org\/10.1596\/978-1-4648-0157-0"},{"key":"8364_CR4","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1504\/IJFIP.2013.058607","volume":"9","author":"M Tiits","year":"2013","unstructured":"Tiits M, Kalvet T (2013) Intelligent piggybacking: a foresight policy tool for small catching-up economies. Int J Foresight Innov Policy 9:253\u2013268","journal-title":"Int J Foresight Innov Policy"},{"key":"8364_CR5","doi-asserted-by":"publisher","first-page":"112","DOI":"10.14254\/2071-8330.2022\/15-4\/7","volume":"15","author":"I Ploom","year":"2022","unstructured":"Ploom I, Kalvet T, Tiits M (2022) Defence industries in small European states: key contemporary challenges and opportunities. J Int Stud 15:112\u2013130","journal-title":"J Int Stud"},{"issue":"1","key":"8364_CR6","first-page":"53","volume":"35","author":"M Tiits","year":"2025","unstructured":"Tiits M, Karo E, Kalvet T (2025) Small countries facing the technological revolution: fostering synergies between economic complexity and foresight research. Compet Rev 35(1):53\u201375","journal-title":"Compet Rev"},{"key":"8364_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.joitmc.2024.100288","volume":"10","author":"M Tiits","year":"2024","unstructured":"Tiits M, Kalvet T, Ounoughi C, Ben Yahia S (2024) Relatedness and product complexity meet gravity models of international trade. J Open Innov Technol Mark Complex 10:100288","journal-title":"J Open Innov Technol Mark Complex"},{"key":"8364_CR8","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.artint.2014.01.001","volume":"209","author":"N Lu","year":"2014","unstructured":"Lu N, Zhang G, Lu J (2014) Concept drift detection via competence models. Artif Intell 209:11\u201328","journal-title":"Artif Intell"},{"key":"8364_CR9","doi-asserted-by":"publisher","unstructured":"Mavromatis I, De Feo S, Khan A (2024) FLAME: Adaptive and Reactive Concept Drift Mitigation for Federated Learning Deployments. arXiv preprint arXiv:2410.01386. https:\/\/doi.org\/10.48550\/arXiv.2410.01386","DOI":"10.48550\/arXiv.2410.01386"},{"key":"8364_CR10","doi-asserted-by":"publisher","unstructured":"\u017dliobait\u0117 I, Pechenizkiy M, Gama J (2016) An overview of concept drift applications. In: Japkowicz N, Stefanowski J (eds) Big Data Analysis: New Algorithms for a New Society. Studies in Big Data, vol 16. Springer, Cham, pp 91\u2013114. https:\/\/doi.org\/10.1007\/978-3-319-26989-4","DOI":"10.1007\/978-3-319-26989-4"},{"key":"8364_CR11","doi-asserted-by":"publisher","first-page":"65","DOI":"10.3390\/bdcc8060065","volume":"8","author":"B Sellami","year":"2024","unstructured":"Sellami B, Ounoughi C, Kalvet T, Tiits M, Rincon-Yanez D (2024) Harnessing graph neural networks to predict international trade flows. Big Data Cogn Comput 8:65","journal-title":"Big Data Cogn Comput"},{"key":"8364_CR12","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1016\/j.patcog.2011.06.019","volume":"45","author":"J Moreno-Torres","year":"2012","unstructured":"Moreno-Torres J, Raeder T, Alaiz-Rodr\u0131guez R, Chawla N, Herrera F (2012) A unifying view on dataset shift in classification. Pattern Recogn 45:521\u2013530","journal-title":"Pattern Recogn"},{"key":"8364_CR13","first-page":"180","volume":"4","author":"D Kifer","year":"2004","unstructured":"Kifer D, Ben-David S, Gehrke J (2004) Detecting change in data streams VLDB 4:180\u2013191","journal-title":"Detecting change in data streams VLDB"},{"key":"8364_CR14","doi-asserted-by":"crossref","unstructured":"Sheskin DJ (2003) Handbook of Parametric and Nonparametric Statistical Procedures, 3rd edn. Chapman & Hall\/CRC, Boca Raton","DOI":"10.1201\/9781420036268"},{"key":"8364_CR15","doi-asserted-by":"publisher","first-page":"12658","DOI":"10.1111\/coin.12658","volume":"40","author":"R Ganesan","year":"2024","unstructured":"Ganesan R, Kaur T, Mittal A, Sahi M, Konar S, Samra T, Puri G, Thingnum S, Auluck N (2024) Application of concept drift detection and adaptive framework for non linear time series data from cardiac surgery. Comput Intell 40:12658","journal-title":"Comput Intell"},{"key":"8364_CR16","unstructured":"Kim T, Kim J, Tae Y, Park C, Choi J-H, Choo J (2022) Reversible instance normalization for accurate time-series forecasting against distribution shift. In: International Conference on Learning Representations . https:\/\/openreview.net\/forum?id=cGDAkQo1C0p"},{"key":"8364_CR17","first-page":"2346","volume":"31","author":"J Lu","year":"2018","unstructured":"Lu J, Liu A, Dong F, Gu F, Gama J, Zhang G (2018) Learning under concept drift: A review. IEEE Trans Knowl Data Eng 31:2346\u20132363","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"8364_CR18","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1023\/A:1018046501280","volume":"23","author":"G Widmer","year":"1996","unstructured":"Widmer G, Kubat M (1996) Learning in the presence of concept drift and hidden contexts. Mach Learn 23:69\u2013101","journal-title":"Mach Learn"},{"key":"8364_CR19","unstructured":"Tinbergen J (1962) Shaping the World Economy: Suggestions for an International Economic Policy. Twentieth Century Fund, New York"},{"key":"8364_CR20","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1257\/000282803321455214","volume":"93","author":"J Anderson","year":"2003","unstructured":"Anderson J, Van Wincoop E (2003) Gravity with gravitas: A solution to the border puzzle. Am Econom Rev 93:170\u2013192","journal-title":"Am Econom Rev"},{"key":"8364_CR21","doi-asserted-by":"crossref","unstructured":"Cho K, Van\u00a0Merri\u00ebnboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. ArXiv Preprint","DOI":"10.3115\/v1\/D14-1179"},{"key":"8364_CR22","unstructured":"Graves A (2013) Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850"},{"key":"8364_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2523813","volume":"46","author":"J Gama","year":"2014","unstructured":"Gama J, \u017dliobait\u0117 I, Bifet A, Pechenizkiy M, Bouchachia A (2014) A survey on concept drift adaptation. ACM Comput Surv 46:1\u201337","journal-title":"ACM Comput Surv"},{"key":"8364_CR24","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1007\/s10618-015-0448-4","volume":"30","author":"G Webb","year":"2016","unstructured":"Webb G, Hyde R, Cao H, Nguyen H, Petitjean F (2016) Characterizing concept drift. Data Mining Knowl Discov 30:964\u2013994","journal-title":"Data Mining Knowl Discov"},{"key":"8364_CR25","unstructured":"Greco S, Vacchetti B, Apiletti D, Cerquitelli T (2024) DriftLens: A concept drift detection tool. In: Proceedings of the 27th International Conference on Extending Database Technology (EDBT), pp 806\u2013809. OpenProceedings"},{"key":"8364_CR26","doi-asserted-by":"crossref","unstructured":"Senarathna D, Tragoudas S, Gowda KN, Schmit M (2023) Detection and quantization of data drift in image classification neural networks. In: 2023 IEEE 24th International Conference on High Performance Switching and Routing (HPSR), 38\u201342 . IEEE","DOI":"10.1109\/HPSR57248.2023.10147972"},{"key":"8364_CR27","doi-asserted-by":"publisher","first-page":"104930","DOI":"10.1016\/j.ijmedinf.2022.104930","volume":"173","author":"K Rahmani","year":"2024","unstructured":"Rahmani K, Thapa R, Tsou P, Chetty S, Barnes G, Lam C, Tso C (2024) Assessing the effects of data drift on the performance of machine learning models used in clinical sepsis prediction. Int J Med Inform 173:104930","journal-title":"Int J Med Inform"},{"key":"8364_CR28","doi-asserted-by":"publisher","first-page":"103397","DOI":"10.1016\/j.aei.2025.103397","volume":"66","author":"D Sato","year":"2025","unstructured":"Sato D, Ruschel E, Scalabrin E, Loures E, Santos E (2025) Interactive process drift detection (ipdd) for condition-based maintenance using process mining. Adv Eng Inform 66:103397","journal-title":"Adv Eng Inform"},{"key":"8364_CR29","first-page":"89","volume":"4","author":"K An","year":"1933","unstructured":"An K (1933) Sulla determinazione empirica di una legge didistribuzione. Giorn Dell\u2019inst Ital Degli Att 4:89\u201391","journal-title":"Giorn Dell\u2019inst Ital Degli Att"},{"key":"8364_CR30","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1214\/aoms\/1177730256","volume":"19","author":"N Smirnov","year":"1948","unstructured":"Smirnov N (1948) Table for estimating the goodness of fit of empirical distributions. Ann Math Stat 19:279\u2013281","journal-title":"Ann Math Stat"},{"key":"8364_CR31","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1080\/01621459.1951.10500769","volume":"46","author":"F Massey Jr","year":"1951","unstructured":"Massey F Jr (1951) The Kolmogorov\u2013Smirnov test for goodness of fit. J Am Stat Assoc 46:68\u201378","journal-title":"J Am Stat Assoc"},{"key":"8364_CR32","unstructured":"Salvador E, Klinger T (2021) Monitoring data drift and model stability in machine learning systems. In: 2021 IEEE International Conference on Big Data"},{"key":"8364_CR33","unstructured":"L\u00f3pez L, Beckmann J (2021) Monitoring ML models: Evaluation of JS divergence for data drift detection. Int J Data Sci 6(3):215\u2013229"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08364-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-026-08364-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08364-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T18:11:12Z","timestamp":1773252672000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-026-08364-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,11]]},"references-count":33,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["8364"],"URL":"https:\/\/doi.org\/10.1007\/s11227-026-08364-1","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,11]]},"assertion":[{"value":"13 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study uses publicly available data from the United Nations Comtrade Database and does not involve human participants or animals. Therefore, ethical approval and informed consent were not required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval and Informed Consent"}}],"article-number":"243"}}