Outlier Detection for Time Series

4 hours of instruction

Detecting outlier data points are powerful machine learning techniques. This covers the concepts and models used in Time Series Analysis, and how these models can be used in anomaly detection. By the end of this course, students will be able to use ARIMA model to identify anomalous data points in a time series dataset.

OBJECTIVES

1. Introduce the concepts used in Time Series analysis
2. Understand the working of time series models and how they can be used in anomaly and outlier detection
3. Develop accurate anomaly detection model using ARIMA

PREREQUISITES

Intermediate Outlier Detection

SYLLABUS & TOPICS COVERED

1. Time series analysis
   • Time series analysis basics
   • AR, MA, and ARIMA models in a nutshell
   • ARIMA use cases and its role in anomaly detection for time series data

SOFTWARE REQUIREMENTS

You will have access to a Python-based JupyterHub environment for this course. No additional download or installation is required.