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Found 11 papers, 10 papers with code
Evaluating Explanation Methods for Multivariate Time Series Classification
In many applications classification alone is not enough, we often need to classify but also understand what the model learns (e. g., why was a prediction given, based on what information in the data).
Back to Basics: A Sanity Check on Modern Time Series Classification Algorithms
The state-of-the-art in time series classification has come a long way, from the 1NN-DTW algorithm to the ROCKET family of classifiers.
An Examination of Wearable Sensors and Video Data Capture for Human Exercise Classification
Additionally, a minimum of 3 IMUs are required to outperform a single camera.
Robust Framework for Explanation Evaluation in Time Series Classification
This paper provides a framework to quantitatively evaluate and rank explanation methods for time series classification.
Classification of cow diet based on milk mid infrared spectra: a data analysis competition at the "International workshop of spectroscopy and chemometrics 2022"
In April 2022, the Vistamilk SFI Research Centre organized the second edition of the "International Workshop on Spectroscopy and Chemometrics - Applications in Food and Agriculture".
Fast and Robust Video-Based Exercise Classification via Body Pose Tracking and Scalable Multivariate Time Series Classifiers
We analyze the accuracy and robustness of BodyMTS and show that it is robust to different types of noise caused by either video quality or pose estimation factors.
Scalable Classifier-Agnostic Channel Selection for Multivariate Time Series Classification
Channel selection is applied as a pre-processing step before training state-of-the-art MTSC algorithms and saves about 70\% of computation time and data storage, with preserved accuracy.
MrSQM: Fast Time Series Classification with Symbolic Representations
The key idea is to transform numerical time series to symbolic representations in the time or frequency domain, i. e., sequences of symbols, and then extract features from these sequences.
Mid infrared spectroscopy and milk quality traits: a data analysis competition at the "International Workshop on Spectroscopy and Chemometrics 2021"
A chemometric data analysis challenge has been arranged during the first edition of the "International Workshop on Spectroscopy and Chemometrics", organized by the Vistamilk SFI Research Centre and held online in April 2021.
Interpretable Time Series Classification using Linear Models and Multi-resolution Multi-domain Symbolic Representations
In this paper we propose new time series classification algorithms to address these gaps.
Interpretable Time Series Classification using All-Subsequence Learning and Symbolic Representations in Time and Frequency Domains
In this work we analyse the state-of-the-art for time series classification, and propose new algorithms that aim to maintain the classifier accuracy and efficiency, but keep interpretability as a key design constraint.
