Anomaly Detection in Time Series Data with Keras
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 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.
Skills you will develop
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
How Guided Projects work
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
Reviews
TOP REVIEWS FROM ANOMALY DETECTION IN TIME SERIES DATA WITH KERAS
It is good. step by step so I can understand. but unfortunately there are no subtitle.
The project was useful. Rhyme's interface needs testing and an upgrade.
I love how well he explained everything and made it simple to follow
Nice course to learn the use of LSMT and autoencoder combined.
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