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Learn about our time series algorithms

• ECG anomaly detection with the LSTM-AD SageMaker Algorithm

Anomaly detection in electrocardiogram (ECG) signals is crucial for early diagnosis and treatment of cardiovascular diseases. With the development of wearable ECG sensors, it has become possible to monitor a patient’s heart condition continuously and in real time. However, it is impracticable for healthcare professional to manually review such a large amount of data…

Anomaly Detection

March 12, 2024

• Control chart pattern clustering with the CNN-KMeans SageMaker Algorithm

Time series clustering is the task of partitioning a set of time series into homogeneous groups. Traditional clustering algorithms based on the Euclidean distance, such as K-Means clustering, are not directly applicable to time series data, as time series with similar patterns can have large Euclidean distance due to misalignments and offsets along the time axis…

Clustering

March 12, 2024

• Arrhythmia classification with the LSTM-FCN SageMaker Algorithm

Arrhythmia classification based on electrocardiogram (ECG) data involves identifying and categorizing abnormal patterns of cardiac electrical activity detected in the ECG signal. Arrhythmia classification is important for diagnosing cardiac abnormalities, assessing the risk of adverse cardiovascular events and guiding appropriate treatment strategies…

Classification

March 5, 2024

• Detecting anomalies in financial time series with the LSTM-AE SageMaker Algorithm

Anomaly detection in financial time series plays a crucial role in identifying unusual market conditions that could affect trading strategies and pose risks to investors. Anomaly detection can help identify abnormal price movements or trading volumes associated with specific events, such as earnings announcements, release of economic indicators, or geopolitical tensions…

Anomaly Detection

January 2, 2024