https://github.com/rakibhhridoy/anomalydetectionintimeseriesdata-keras
Statistics, signal processing, finance, econometrics, manufacturing, networking[disambiguation needed] and data mining, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.
https://github.com/rakibhhridoy/anomalydetectionintimeseriesdata-keras
anomaly-detection autoencoder autoencoders deep-learning dropout keras lstm machine-learning numpy pandas python regularization tensorflow threshold
Last synced: 3 months ago
JSON representation
Statistics, signal processing, finance, econometrics, manufacturing, networking[disambiguation needed] and data mining, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.
- Host: GitHub
- URL: https://github.com/rakibhhridoy/anomalydetectionintimeseriesdata-keras
- Owner: rakibhhridoy
- Created: 2020-07-17T16:41:40.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2020-08-18T14:31:10.000Z (almost 5 years ago)
- Last Synced: 2025-03-23T23:27:05.167Z (3 months ago)
- Topics: anomaly-detection, autoencoder, autoencoders, deep-learning, dropout, keras, lstm, machine-learning, numpy, pandas, python, regularization, tensorflow, threshold
- Language: Jupyter Notebook
- Homepage: https://rakibhhridoy.github.io
- Size: 7.6 MB
- Stars: 17
- Watchers: 0
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md