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Z-Time: efficient and effective interpretable multivariate time series classification
Lee, Zed,
Lindgren, Tony,
and Papapetrou, Panagiotis
Data mining and knowledge discovery,
2023
|
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Finding Local Groupings of Time Series
Lee, Zed,
Trincavelli, Marco,
and Papapetrou, Panagiotis
In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part VI,
2023
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Z-Hist: A Temporal Abstraction of Multivariate Histogram Snapshots
Lee, Zed,
Anton, Nicholas,
Papapetrou, Panagiotis,
and Lindgren, Tony
In International Symposium on Intelligent Data Analysis (IDA),
2021
|
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SMILE: a feature-based temporal abstraction framework for event-interval sequence classification
Rebane, Jonathan,
Karlsson, Isak,
Bornemann, Leon,
and Papapetrou, Panagiotis
Data Mining and Knowledge Discovery,
2021
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Z-Miner: An Efficient Method for Mining Frequent Arrangements of Event
Intervals
Lee, Zed,
Lindgren, Tony,
and Papapetrou, Panagiotis
In International Conference on Knowledge Discovery
and Data Mining (SIGKDD),
2020
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Locally and globally explainable time series tweaking
Karlsson, Isak,
Rebane, Jonathan,
Papapetrou, Panagiotis,
and Gionis, Aristides
Knowledge and Information Systems,
2020
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Corrigendum to ’Learning from heterogeneous temporal data in electronic
health records’. [J. Biomed. Inform. 65 (2017) 105-119]
Zhao, Jing,
Papapetrou, Panagiotis,
Asker, Lars,
and Bostrom, Henrik
J. Biomed. Informatics,
2020
|
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Exploiting complex medical data with interpretable deep learning for adverse drug event prediction
Rebane, Jonathan,
Samsten, Isak,
and Papapetrou, Panagiotis
Artificial Intelligence in Medicine,
2020
|
|
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A classification framework for exploiting sparse multi-variate temporal
features with application to adverse drug event detection in medical
records
Bagattini, Francesco,
Karlsson, Isak,
Rebane, Jonathan,
and Papapetrou, Panagiotis
BMC Medical Informatics Decis. Mak.,
2019
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An Investigation of Interpretable Deep Learning for Adverse Drug Event
Prediction
Rebane, Jonathan,
Karlsson, Isak,
and Papapetrou, Panagiotis
In International Symposium on Computer-Based Medical Systems (CBMS),
2019
|
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User Traffic Prediction for Proactive Resource Management: Learning-Powered
Approaches
Azari, Amin,
Papapetrou, Panagiotis,
Denic, Stojan Z.,
and Peters, Gunnar
In Global Communications Conference (GLOBECOM),
2019
|
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User Traffic Prediction for Proactive Resource Management: Learning-Powered
Approaches
Azari, Amin,
Papapetrou, Panagiotis,
Denic, Stojan Z.,
and Peters, Gunnar
In In Global Communications Conference (GLOBECOM),
2019
|
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Cellular Traffic Prediction and Classification: A Comparative Evaluation
of LSTM and ARIMA
Azari, Amin,
Papapetrou, Panagiotis,
Denic, Stojan Z.,
and Peters, Gunnar
In In International Conference on Discovery Science (DS),
2019
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Explainable time series tweaking via irreversible and reversible temporal transformations
Karlsson, Isak,
Rebane, Jonathan,
Papapetrou, Panagiotis,
and Gionis, Aristides
In International Conference on Data Mining (ICDM),
2018
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On searching and indexing sequences of temporal intervals
Kostakis, Orestis,
and Papapetrou, Panagiotis
Data Min. Knowl. Discov.,
2017
|
|
|
Learning from heterogeneous temporal data in electronic health records
Zhao, Jing,
Papapetrou, Panagiotis,
Asker, Lars,
and Boström, Henrik
J. Biomed. Informatics,
2017
|
|
|
Kapminer: Mining ordered association rules with constraints
Karlsson, Isak,
Papapetrou, Panagiotis,
and Asker, Lars
In International Symposium on Intelligent Data Analysis,
2017
|
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|
Generalized random shapelet forests
Karlsson, Isak,
Papapetrou, Panagiotis,
and Boström, Henrik
Data mining and knowledge discovery,
2016
|
|
|
Early random shapelet forest
Karlsson, Isak,
Papapetrou, Panagiotis,
and Boström, Henrik
In International Conference on Discovery Science (DS),
2016
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