Zielonogórska Biblioteka Cyfrowa udostępnia 67 138 obiektów cyfrowych
Obiekt
Tytuł: A review of explainable semi-supervised methods in multivariate time series analysis
Autor:
Wichrowski, Filip ; Ostrowski, Marcin ; Boratyn, Marta ; Kaczmarek-Majer, Katarzyna
Data wydania:
Typ zasobu:
Współtwórca:
Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.
Tytuł publikacji grupowej:
Abstract:
Recent advances in information and communication technologies have led to the widespread use of smart meters, wearable sensors, and related devices in healthcare, industrial monitoring (e.g., manufacturing and energy systems), and transportation. These systems generate large volumes of sequential data, yet fully annotating them remains expensive and often impractical. Consequently, only a small fraction of the data is typically labeled, limiting the applicability of fully supervised learning. At the same time, ignoring available labels altogether, as in fully unsupervised approaches, risks discarding valuable information. ; This tension has motivated growing interest in semi-supervised methods that learn from both labeled and unlabeled data. However, many such approaches rely on complex black-box models, making the decision-making process opaque, particularly in high-risk domains such as medicine and finance. Explainable AI (XAI) has therefore become essential for building trust and ensuring accountability in these settings. ; This review surveys recent advances in explainable semi-supervised methods for multivariate time-series analysis. We introduce a taxonomy based on how explainability is integrated, categorizing the approaches as white-box (transparent), post hoc (opaque), and intermediate. Within each category, we further classify them by their most distinctive characteristics: the model class for white-box, the interpretability integration strategy for intermediate, and the explanation technique for post-hoc. ; We also discuss common practices and lessons learned in dealing with partial supervision and model interpretability, and highlight key challenges in sequential data analysis, such as the choice of performance metrics and explanation techniques. We find that, although interest in explainable semi-supervised time-series methods is growing, the systematic evaluation of explanations remains underdeveloped and lacks standardized evaluation practices. Nearly 70% of the reviewed works report some form of explainability validation; however, it is typically indirect, qualitative, or limited in scope. Overall, explainable semi-supervised methods represent a promising direction for future research, with potential benefits across a wide range of real-world time-series applications.
Wydawca:
Zielona Góra: Uniwersytet Zielonogórski
Identyfikator zasobu:
DOI:
Strony:
Źródło:
AMCS, volume 36, number 2 (2026) ; kliknij tutaj, żeby przejść
Jezyk:
Licencja CC BY 4.0:
Prawa do dysponowania publikacją:
Biblioteka Uniwersytetu Zielonogórskiego
Kolekcje, do których przypisany jest obiekt:
- Zielonogórska Biblioteka Cyfrowa > Repozytorium > Jednostki organizacyjne > Wydział Informatyki, Elektrotechniki i Automatyki
- Zielonogórska Biblioteka Cyfrowa > Repozytorium > Typy utworów > Artykuły
- Zielonogórska Biblioteka Cyfrowa > Repozytorium > Czasopisma naukowe i serie wydawnicze UZ > International Journal of Applied Mathematics and Computer Science (AMCS) > International Journal of Applied Mathematics and Computer Science (AMCS) (2026)
Data ostatniej modyfikacji:
17 cze 2026
Data dodania obiektu:
17 cze 2026
Liczba wyświetleń treści obiektu:
0
Wszystkie dostępne wersje tego obiektu:
https://zbc.uz.zgora.pl/publication/108348
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| Nazwa wydania | Data |
|---|---|
| A review of explainable semi-supervised methods in multivariate time series analysis | 17 cze 2026 |
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