We introduce the Plug-Level Appliance Identification Dataset (PLAID), a public and crowd-sourced dataset for load identification research consisting of short voltage and current measurements (in the order of a few seconds) for different residential appliances. The goal of PLAID is to provide a public library for high-resolution appliance measurements that can be integrated into existing or novel appliance identification algorithms. PLAID currently contains measurements for more than 200 different appliance instances, representing 11 appliance classes, and totaling more than a thousand records. In this demo we summarize the existing dataset, demonstrate how new records can be added to the library using a web interface and, finally, walk through a live example of how the library can be integrated into an existing non-intrusive load monitoring (NILM) algorithm framework.
Deep learning performs remarkably well on many time series analysis tasks recently. The superior performance of deep neural networks relies heavily on a large number of training data to avoid overfitting. However, the labeled data of many real-world time series applications may be limited such as classification in medical time series and anomaly detection in AIOps. As an effective way to enhance the size and quality of the training data, data augmentation is crucial to the successful application of deep learning models on time series data. In this paper, we systematically review different data augmentation methods for time series. We propose a taxonomy for the reviewed methods, and then provide a structured review for these methods by highlighting their strengths and limitations. We also empirically compare different data augmentation methods for different tasks including time series classification, anomaly detection, and forecasting. Finally, we discuss and highlight five future directions to provide useful research guidance.
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