Feb. 12, 2024, 5:41 a.m. | Payal Mohapatra Lixu Wang Qi Zhu

cs.LG updates on arXiv.org arxiv.org

Monitoring and recognizing patterns in continuous sensing data is crucial for many practical applications. These real-world time-series data are often nonstationary, characterized by varying statistical and spectral properties over time. This poses a significant challenge in developing learning models that can effectively generalize across different distributions. In this work, based on our observation that nonstationary statistics are intrinsically linked to the phase information, we propose a time-series learning framework, PhASER. It consists of three novel elements: 1) phase augmentation that …

applications challenge continuous cs.lg data domain eess.sp monitoring patterns practical sensing series statistical time series work world

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