Time Series
This dataset includes the relevant data for the journal article titled 'A Novel LSTM Pipeline to Detect Anomalies in Manufacturing Production'. In this paper, we present a novel anomaly detection method using a semi-supervised LSTM forecasting approach to highlight process anomalies in a complex, real-world dataset in an automotive manufacturing setting. This data includes two time-series subsets, each with 5000 labeled observations.
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time-series, accelerometer, gyroscope-to-yaw, acoustic localization
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This dataset is composed of 4-Dimensional time series files, representing the movements of all 38 participants during a novel control task. In the ‘5D_Data_Extractor.py’ file this can be set up to 6-Dimension, by the ‘fields_included’ variable. Two folders are included, one ready for preprocessing (‘subjects raw’) and the other already preprocessed ‘subjects preprocessed’.
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