Instructions to use keras-io/time-series-anomaly-detection-autoencoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use keras-io/time-series-anomaly-detection-autoencoder with TF-Keras:
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy) # See https://github.com/keras-team/tf-keras for more details. from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("keras-io/time-series-anomaly-detection-autoencoder") - Notebooks
- Google Colab
- Kaggle
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Download README.md from keras-io/time-series-anomaly-detection-autoencoder: direct link, hf CLI and curl.
- Browser
- Download file 569 Bytes
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https://huggingface.co/keras-io/time-series-anomaly-detection-autoencoder/resolve/main/README.md
- Command line
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hf download hf://keras-io/time-series-anomaly-detection-autoencoder/README.md
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curl -L -o README.md https://huggingface.co/keras-io/time-series-anomaly-detection-autoencoder/resolve/main/README.md
569 Bytes
metadata
tags:
- autoencoder
- time series
- anomaly detection
license:
- cc0-1.0
Keras Implementation of time series anomaly detection using an Autoencoder ⌛
This repo contains the model and the notebook for this time series anomaly detection implementation of Keras.
Full credits to: Pavithra Vijay
Background Information
This notebook demonstrates how you can use a reconstruction convolutional autoencoder model to detect anomalies in timeseries data.