In the real world, time series data is one of the most used and researched types of data, and anomaly detection in it has innumerable uses ranging from detecting fraud transactions, uncovering fraudulent insurance claims, and even detecting critical equipment failures. In this course, Build a Model for Anomaly Detection in Time Series Data, you’ll learn different techniques to build a model for anomaly detection on your very own time series dataset. First, you’ll be introduced to time series data and its different components, what anomaly detection means when it pertains to a time series dataset, and its importance. Next, you’ll discover different techniques with which to build models that detect anomalies in time series datasets. Finally, you’ll learn how to deal with the anomalies that you previously detected in your dataset. When you’re finished with this course, you’ll have the skills and knowledge needed to explore, clean, prepare, and detect anomalies on your own time series dataset.
Author Name: Pratheerth Padman
Author Description:
Pratheerth is a Data Scientist who has entered the field after an eclectic mix of educational and work experiences. He has a Bachelor’s in Engineering in Mechatronics from India, Masters in Engineering Management from Australia and then a couple of years of work experience as a Production Engineer in the Middle East. Then when the A.I bug bit him, he dropped everything to dedicate his life to the field. He is currently working on mentoring, course creation and freelancing as a Data Scientist.

Table of Contents

• Course Overview
  1min
• Introduction to Time Series Data and Anomaly Detection
  23mins
• Building a Model to Automate Anomaly Detection
  37mins
• Model Evaluation and Dealing with Anomalies
  10mins