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Time series classification [PDF]

, 2022
Time series classification is a machine learning subfield with numerous real-world applications. Standard machine learning algorithms are typically not well suited to work on raw time series as the input data is, in general, temporally structured ...
Omerovic, Ajdin
core   +1 more source

Sparseness-Optimized Feature Importance for Time Series Classification

IEEE Access
The literature reports a wide variety of attribution methods for explaining the predictions made by time series classification (TSC) algorithms. These post-hoc explanation methods span from model-specific to agnostic procedures that operate at different ...
Isel Grau, Gonzalo Napoles, Agnieszka Jastrzebska, Yamisleydi Salgueiro   +3 more
doaj   +1 more source

Time series classification in reservoir- and model-space: A comparison

, 2022
S.197-208Learning in the space of Echo State Network (ESN) output weights, i.e. model space, has achieved excellent results in time series classification, visualization and modelling.
Reinhart, René Felix, Steil, Jochen, Aswolinskiy, Witali   +2 more
core   +1 more source

Language, Culture, and Cancer: Qualitative Insights Into Communication Disparities Among Spanish‐Speaking Caregivers of Children With Cancer

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Latino children are projected to make up nearly one‐third of United States (US) children by 2060, and many of their caregivers speak Spanish. Prior survey research has documented communication difficulties for Spanish‐speaking caregivers of children with cancer, but contemporary qualitative data are limited.
Jenny Ruiz, Alejandra Curbelo‐Paz, Sonia Morales, Elizabeth Contreras, Amy Houtrow, Scott H. Maurer, Judy C. Chang   +6 more
wiley   +1 more source

Non-Parametric Time Series Classification [PDF]

Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2006
We present an improved state-based prediction algorithm for time series. Given time series produced by a process composed of different underlying states, the algorithm predicts future time series values based on past time series values for each state. Unlike many algorithms, this algorithm predicts a multi-modal distribution over future values.
Scott Lenser, Manuela M. Veloso
openaire   +1 more source

Biphasic Clinical Course of Pediatric Cytomegalovirus‐Associated Immune Thrombocytopenia: Early Poor Treatment Response but Favorable Long‐Term Outcomes

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Cytomegalovirus (CMV) is a recognized trigger of immune thrombocytopenia (ITP); however, its incidence and impact on disease course in children remain controversial. Therefore, we aimed to characterize the clinical course of pediatric patients with CMV‐associated ITP.
Oded Gilad, Ohad Atia, Osama Tanous, Dafna Brik Simon, Michal Dvori, Miriam Cohen, Tracie A. Goldberg, Efraim Bilavsky, Joanne Yacobovich, Orna Steinberg‐Shemer   +9 more
wiley   +1 more source

An Effective Confidence-Based Early Classification of Time Series

IEEE Access, 2019
Early classification of time series aims to predict the class value of a sequence accurately as early as possible, not wait for the full-length data, which is significant in many time-sensitive applications and has attracted great interest in recent ...
Junwei Lv, Xuegang Hu, Lei Li, Peipei Li
doaj   +1 more source

Chemotherapy for Spinal Cord Intramedullary Pediatric Low‐Grade Glioma—A Systematic Literature Review

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Pediatric intramedullary low‐grade gliomas (PIMLGGs) are rare neoplasms that present unique clinical and management challenges. Although surgery remains the primary tool for tissue diagnosis and decompression, complete gross‐total resection is frequently unfeasible due to the infiltrative nature of these lesions.
Olga M. Sergeenko, Segev Gabay, Jonathan Roth, Amit Ringel, Rina Dvir, Shlomi Constantini   +5 more
wiley   +1 more source

LOMATCE: LOcal Model-Agnostic Time Series Classification Explanations

IEEE Access
Deep learning models perform exceptionally well in time series classification, but their lack of interpretability remains a significant challenge. Although explainable AI (XAI) techniques are well established for image and tabular data, adapting these ...
Ephrem Tibebe Mekonnen, Luca Longo, Pierpaolo Dondio   +2 more
doaj   +1 more source

Bone and Soft Tissue Neoplasms in Infants Versus Older Children: A Report From the Cancer in Young People in Canada Program

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background and Aims Infant bone and soft tissue neoplasms present distinct clinical challenges and lack large‐scale data to inform treatment decisions. This study aims to characterize bone and soft tissue neoplasms in infants in Canada and to compare their outcomes with those of older pediatric patients.
Samuel Sassine, Hallie Coltin, Maria Kondyli, Monia Marzouki, Nicolas Prud'homme, Nida Usmani, Rebecca J. Deyell, Lesleigh Abbott, Tony H. Truong, Sapna Oberoi, Ketan Kulkarni, Josée Brossard, Lynette Bowes, Paul Gibson, Sarah McKillop, Roona Sinha, Lillian Sung, Catherine Vezina, Laura Wheaton, Alexandra P. Zorzi, Marie‐Claude Pelland‐Marcotte, Thai Hoa Tran   +21 more
wiley   +1 more source