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Time Series Anomaly Detection Training

Anomaly Detection Training

Due to the COVID-19 our training courses will be taught via an online classroom.

Receive in-depth knowledge from industry professionals, test your skills with hands-on assignments & demos, and get access to valuable resources and tools.

This course is an introduction into anomaly detection. In data analysis, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. The training mainly focus on how to detect anomalies across many time series, such as sensor data. After this course, you will be able to detect anomalous behaviour such as faulty bearings, based on their vibration measurements. As requirements, experience with R or Python are necessary in order to follow the training and do the assignments.

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Academy: Anomaly Detection
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About the training & classes

The Anomaly Detection training is split in 2 days. Click below to see a detailed description of each class: 

 
 
Local Models

This training focuses on distance or density-based machine learning models that primarily take into account the local character of data points. Encompassing Density Estimation, Nearest Neighbors and Clustering, these algorithms are necessary tools in our Machine Learning toolkit.

The Local Models training starts by introducing the topic of Local Models and their applications in supervised as well as in unsupervised machine learning. We then discuss the relevance of different distance metrics and normalization methods, before delving into Density Estimation methods such as Kernel Density Estimation as well as parametric alternatives. Continuing to nearest neighbors algorithms like k-Nearest Neighbors and Approximate Nearest Neighbors, we finally arrive at unsupervised methods for Clustering, such as k-Means, Expectation Maximization and DBSCAN. This theoretical knowledge is applied in practice during a two-part lab session.

The training includes theory, demos, and hands-on exercises. After this training you have gained knowledge about:

  • Distance Metrics
  • Normalization
  • Density Estimation
  • k-Nearest Neighbors
  • Approximate Nearest Neighbors
  • Clustering
  • k-Means
  • Expectation-Maximization
  • Hierarchical Clustering
  • DBSCAN
Anomaly Detection (R or Python)

The training starts with the definition of anomaly and some applications of anomaly detection. Then we talk about anomaly detection in the context of time series where we mainly focus on how to detect anomalies across many time series. After that the two main types of anomaly detection are discussed: supervised and unsupervised. Then we dive into the unsupervised anomaly detection algorithms and explain some of them in more detail. Finally we discuss data preprocessing steps and performance evaluation metrics.

In the lab exercise we apply the acquired knowledge to find faulty bearings, based on their vibration measurements. We do this by extracting features from the vibration time series and using unsupervised anomaly detection methods. The lab is available both in R and Python languages.

The training includes theory and hands-on exercises. After this training you will have gained knowledge about:

  • Anomaly detection within and across time series
  • Extract features from time series
  • Types of anomaly detection: supervised, unsupervised
  • Some methods for supervised detection
  • Unsupervised anomaly detection algorithms
  • Clustering based
  • Kernel density estimation and Gaussian mixture model
  • One-class SVM
  • KNN-based methods, Local Outlier Factor
  • Isolation forest
  • Preprocessing steps, like normalization and PCA
  • Evaluation metrics: AUC, precision, recall