Anomaly Detection in Time Series Data with Keras

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Anomaly Detection in Time Series Data with Keras Specifically, we will be designing and training an LSTM autoencoder using the Keras API with Tensorflow 2 as the backend to detect anomalies

In this Guided Project, you will:

Build an LSTM Autoencoder in Keras

Detect anomalies with Autoencoders in time series data

Create interactive charts and plots with Plotly and Seaborn

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

Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

Project Overview and Import Libraries

Load and Inspect the S&P 500 Index Data

Data Preprocessing

Temporalize Data and Create Training and Test Splits

Build an LSTM Autoencoder

Train the Autoencoder

Plot Metrics and Evaluate the Model

Detect Anomalies in the S&P 500 Index Data

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