Patch-based self-supervised learning device and method for anomaly detection in multivariate time series data

A patch-based self-supervised learning device for anomaly detection in multivariate time series data, the device comprising:

• a memory configured to store at least one process for performing an anomaly detection operation on the multivariate time series data;
• and
• a processor configured to perform the anomaly detection operation on the multivariate time series data based on the at least one process,
• wherein the processor includes:
• a pre-training unit configured to pre-train a model so as to restore the multivariate time series data by dividing the multivariate time series data into a plurality of patches, generating a patch, which is randomly masked, from among the plurality of patches, and reconstructing the masked patch;
• and
• a downstream unit configured to perform self-learning so as to divide the multivariate time series data into the plurality of patches, randomly apply anomaly enhancement to the plurality of patches, and detect a patch, to which the anomaly enhancement is applied, by using the pre-trained model.