Anomaly Detection in Time Series Data with Keras

このGuided Projectでは、次のことを行います。

Build an LSTM Autoencoder in Keras

Detect anomalies with Autoencoders in time series data

Create interactive charts and plots with Plotly and Seaborn

Clock1.5 hours
Comment Dots英語

In this hands-on introduction to anomaly detection in time series data with Keras, you and I will build an anomaly detection model using deep learning. Specifically, we will be designing and training an LSTM autoencoder using the Keras API with Tensorflow 2 as the backend to detect anomalies (sudden price changes) in the S&P 500 index. We will also create interactive charts and plots using Plotly Python and Seaborn for data visualization and display our results in Jupyter notebooks. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and Keras pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.


Deep LearningMachine LearningData Visualization (DataViz)Anomaly Detectionkeras



  1. Project Overview and Import Libraries

  2. Load and Inspect the S&P 500 Index Data

  3. Data Preprocessing

  4. Temporalize Data and Create Training and Test Splits

  5. Build an LSTM Autoencoder

  6. Train the Autoencoder

  7. Plot Metrics and Evaluate the Model

  8. Detect Anomalies in the S&P 500 Index Data

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