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absaw / Surface-Water-Quality-Data-Anomaly-Detection

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Surface water quality data analysis and prediction of Potomac River, West Virginia, USA. Using time series forecasting, and anomaly detection : ARIMA, SARIMA, Isolation Forest, OCSVM and Gaussian Distribution

arima time-series-analysis gaussian-distribution anomaly-detection sarimax isolation-forest isolation-forest-algorithm time-series-forecasting iforest oneclasssvm iforest-model anomaly-detection-models
  • Updated Jun 6, 2020
  • Jupyter Notebook

nafiul-araf / Anomaly-Detection

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Anomaly detection (also known as outlier analysis) is a data mining step that detects data points, events, and/or observations that differ from the expected behavior of a dataset. A typical data might reveal significant situations, such as a technical fault, or prospective possibilities, such as a shift in consumer behavior.

visualization neural-network statistical-analysis outliers cnn-keras anomaly-detection zscore knn-classification local-outlier-factor one-class-svm iforest-model pyod autoencoder-neural-network inliers anomoly-score minimum-covariance
  • Updated Dec 19, 2021
  • Jupyter Notebook

Wb-az / timeseries-sensor-anomaly-detection

Star 3

Unsupervised anomaly detection in vibration signal using PyCaret vs BiLSTM

deep-learning time-series pytorch hypothesis-testing svm-model clustering-algorithm experimental-design pairwise-comparison knn-model nonparametric-statistics iforest-model conover bilstm-model pycaret-library histogram-based-outlier-score friedman-test unsupervides-deep-learning minimum-covariance-determinant
  • Updated Feb 20, 2024
  • Jupyter Notebook

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