首页|Researchers at Beijing University of Posts and Telecommunications Release New St udy Findings on Artificial Intelligence (Time-series representation learning via Time-Frequency Fusion Contrasting)
Researchers at Beijing University of Posts and Telecommunications Release New St udy Findings on Artificial Intelligence (Time-series representation learning via Time-Frequency Fusion Contrasting)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news originating from Beijing , People's Republic of China, by NewsRx editors, the research stated, "Time seri es is a typical data type in numerous domains; however, labeling large amounts o f time series data can be costly and time-consuming. Learning effective represen tation from unlabeled time series data is a challenging task." The news journalists obtained a quote from the research from Beijing University of Posts and Telecommunications: "Contrastive learning stands out as a promising method to acquire representations of unlabeled time series data. Therefore, we propose a self-supervised time-series representation learning framework via Time -Frequency Fusion Contrasting (TF-FC) to learn time-series representation from u nlabeled data. Specifically, TF-FC combines time-domain augmentation with freque ncy-domain augmentation to generate the diverse samples. For time-domain augment ation, the raw time series data pass through the time-domain augmentation bank ( such as jitter, scaling, permutation, and masking) and get time-domain augmentat ion data. For frequency-domain augmentation, first, the raw time series undergoe s conversion into frequency domain data following Fast Fourier Transform (FFT) a nalysis. Then, the frequency data passes through the frequency-domain augmentati on bank (such as low pass filter, remove frequency, add frequency, and phase shi ft) and gets frequency-domain augmentation data. The fusion method of time-domai n augmentation data and frequency-domain augmentation data is kernel PCA, which is useful for extracting nonlinear features in high-dimensional spaces. By captu ring both the time and frequency domains of the time series, the proposed approa ch is able to extract more informative features from the data, enhancing the mod el's capacity to distinguish between different time series."
Beijing University of Posts and Telecomm unicationsBeijingPeople's Republic of ChinaAsiaArtificial IntelligenceMachine Learning
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