Tag: Autoencoder
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Returns-Volatility Domain K-Means Clustering and LSTM Anomaly Detection of S&P 500 Stocks
This study aims to implement and evaluate the K-means algorithm for ranking/clustering S&P 500 stocks based on average annualized return and volatility. The second goal is to detect anomalies in the best performing S&P 500 stocks using the Isolation Forest algorithm. Additionally, anomalies in the S&P 500 historical stock price time series data will be…
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Real-Time Anomaly Detection of NAB Ambient Temperature Readings using the TensorFlow/Keras Autoencoder
The content covers a detailed guide on implementing anomaly detection in time series data using autoencoders. The tutorial utilizes Python and real-world temperature dataset from Numenta Anomaly Benchmark (NAB). Following the Python workflow, the algorithm imports required libraries, performs anomaly detection, and visualizes anomalies. A trained autoencoder model identifies anomalies, with Precision, Recall, and F1…
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Early Heart Attack Prediction using ECG Autoencoder and 19 ML/AI Models with Test Performance QC Comparisons
Globally, cardiovascular disease (CVD) is the primary cause of morbidity and mortality, accounting for more than 70% of all fatalities. Machine learning (ML) can be used to predict the risk of a heart attack. The algorithms used for this task would be supervised ML algorithms, such as Random Forest, Logistic Regression, Support Vector Machines, etc.…
