Time series anomaly detection using LSTM autoencoder in ECG data is a technique that involves the use of a specific type of deep neural network, known as a Long Short-Term Memory (LSTM) autoencoder, ...

can be very effective for detecting anomalies in ECG image time-series data and similar problems. The proposed approach demonstrates the effectiveness of autoencoder-based anomaly detection for image ...

Abstract: In this paper, we present a new anomaly detection method for time-series data in complex systems such as power grid and cellular networks. The proposed anomaly detection method is developed ...

Therefore, the autoencoder input and output both have 65 values -- 64 pixel grayscale values (0 to 16) plus a label (0 to 9). Notice that the demo program analyzes both the predictors (pixel values) ...

Running prepare_data.py will generate contextual and point anomalies (using the Gaussian Mixture Model and Multivariate Uniform Distribution methods, as described in the paper) and inject them into ...