Related papers: Learning from Time Series under Temporal Label Noise

Learning from Time Series under Temporal Label Noise

URL: http://arxiv.org/abs/2402.04398v1
Date: Tue, 6 Feb 2024 20:56:31 GMT
Title: Learning from Time Series under Temporal Label Noise
Authors: Sujay Nagaraj, Walter Gerych, Sana Tonekaboni, Anna Goldenberg, Berk Ustun, Thomas Hartvigsen
Abstract summary: We first propose and formalize temporal label noise, an unstudied problem for sequential classification of time series. We show that our methods lead to state-of-the-art performance in the presence of diverse temporal label noise functions using real and synthetic data.
Score: 23.39598516168891
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Many sequential classification tasks are affected by label noise that varies over time. Such noise can cause label quality to improve, worsen, or periodically change over time. We first propose and formalize temporal label noise, an unstudied problem for sequential classification of time series. In this setting, multiple labels are recorded in sequence while being corrupted by a time-dependent noise function. We first demonstrate the importance of modelling the temporal nature of the label noise function and how existing methods will consistently underperform. We then propose methods that can train noise-tolerant classifiers by estimating the temporal label noise function directly from data. We show that our methods lead to state-of-the-art performance in the presence of diverse temporal label noise functions using real and synthetic data.

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