Introduction

Anomaly detection in time series data is a crucial aspect of identifying irregularities that may signify potential problems or opportunities across a range of industries, from finance and healthcare to manufacturing and cybersecurity. This 5-day training course will delve into the various techniques and tools used to detect anomalies in time series data, using both traditional statistical methods and modern machine learning approaches. Participants will gain hands-on experience in detecting outliers, detecting trend shifts, and identifying seasonal anomalies using real-world datasets.

Objectives

By the end of this course, participants will:

1. Understand the core concepts of anomaly detection and its importance in time series data.
2. Learn how to preprocess and visualize time series data for anomaly detection.
3. Implement statistical and machine learning-based anomaly detection methods.
4. Build and evaluate models for detecting outliers, point anomalies, and contextual anomalies.
5. Use unsupervised learning techniques such as clustering and autoencoders for anomaly detection.
6. Explore advanced topics like seasonality, trends, and multi-dimensional anomalies.
7. Deploy anomaly detection systems in real-world use cases (finance, IoT, and security).
8. Understand best practices for evaluating anomaly detection models’ performance.

Who Should Attend?

• Data Scientists & Analysts interested in time series analysis.
• Machine Learning Engineers focused on anomaly detection techniques.
• Operations and Monitoring Teams looking to improve real-time anomaly detection.
• Business Analysts and professionals working with financial, healthcare, and IoT time series data.
• Anyone interested in detecting unusual patterns in sequential data.

Course Outline (5 Days)

Day 1: Introduction to Time Series and Anomaly Detection

Morning Session

• Introduction to Time Series Data

  • What is time series data?
  • Components of time series: trend, seasonality, noise, and cyclic behavior
  • Time series applications across industries: finance, manufacturing, healthcare, IoT
  • Hands-on: Visualizing time series data and identifying patterns

• Anomaly Detection Fundamentals

  • Types of anomalies: point anomalies, contextual anomalies, and collective anomalies
  • Overview of supervised vs. unsupervised anomaly detection
  • Common methods for anomaly detection: statistical techniques, machine learning, deep learning
  • Hands-on: Identifying point anomalies using simple visualization techniques

Afternoon Session

• Data Preprocessing for Time Series

  • Handling missing data, outliers, and normalization
  • Time series decomposition (trend, seasonality, residuals)
  • Feature engineering: lags, rolling statistics, and window functions
  • Hands-on: Preprocessing and visualizing time series data for anomaly detection

• Hands-on Exercise

  • Preprocessing and decomposing financial time series data for anomaly detection

Day 2: Statistical Methods for Anomaly Detection

Morning Session

• Statistical Approaches to Anomaly Detection

  • Z-score, Grubbs’ test, and Modified Z-score for detecting outliers
  • Autoregressive Integrated Moving Average (ARIMA) models and their use in anomaly detection
  • Detecting residual anomalies using ARIMA models
  • Hands-on: Building an ARIMA model for detecting anomalies in a time series

• Moving Average & Exponential Smoothing

  • Moving average models (Simple, Weighted, Exponential) for anomaly detection
  • Exponential Smoothing (ETS) and its application in time series forecasting and anomaly detection
  • Hands-on: Implementing Exponential Moving Average for anomaly detection in e-commerce data

Afternoon Session

• Contextual Anomaly Detection

  • Anomalies based on seasonality and periodicity
  • Using Seasonal Decomposition of Time Series (STL) to detect seasonal anomalies
  • Hands-on: Using STL decomposition to detect anomalies in seasonal data

• Hands-on Exercise

  • Applying ARIMA and Moving Averages to detect outliers in sensor data

Day 3: Machine Learning-Based Anomaly Detection

Morning Session

• Unsupervised Machine Learning Methods

  • Clustering techniques: K-Means, DBSCAN, and Isolation Forest for anomaly detection
  • Using density-based techniques for detecting anomalies in multivariate time series
  • Hands-on: Clustering time series data and detecting anomalies with K-Means

• Dimensionality Reduction for Anomaly Detection

  • Techniques like Principal Component Analysis (PCA) and t-SNE for anomaly detection in high-dimensional time series
  • Reducing dimensionality to improve the efficiency of anomaly detection models
  • Hands-on: Applying PCA for anomaly detection on high-dimensional IoT sensor data

Afternoon Session

• Autoencoders for Anomaly Detection

  • Understanding autoencoders and their use in unsupervised anomaly detection
  • Training an autoencoder neural network for time series anomaly detection
  • Hands-on: Building an autoencoder model for detecting anomalies in time series data

• Hands-on Exercise

  • Detecting anomalies in temperature sensor data using autoencoders

Day 4: Advanced Techniques for Anomaly Detection

Morning Session

• Deep Learning for Time Series Anomaly Detection

  • Introduction to Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks
  • Using LSTMs for modeling time series and detecting anomalies
  • Hands-on: Building an LSTM-based anomaly detection model on stock market data

• Modeling Seasonal and Trend-based Anomalies

  • Understanding seasonal and trend decomposition using deep learning methods
  • Detecting seasonal shifts and trend-based anomalies in time series
  • Hands-on: Building a deep learning model for detecting seasonal anomalies in sales data

Afternoon Session

• Ensemble Methods for Robust Anomaly Detection

  • Combining models: Random Forest, Gradient Boosting Machines for anomaly detection
  • Boosting the performance of anomaly detection using stacking and bagging
  • Hands-on: Using ensemble methods for detecting anomalies in multivariate time series

• Hands-on Exercise

  • Implementing ensemble models to detect anomalies in sensor data streams

Day 5: Deployment, Evaluation, and Real-World Use Cases

Morning Session

• Evaluating Anomaly Detection Models

  • Common metrics for evaluation: precision, recall, F1 score, ROC curve, and AUC
  • Cross-validation techniques for time series data: walk-forward validation
  • Hands-on: Evaluating the performance of your anomaly detection models

• Deploying Anomaly Detection Systems

  • Building scalable anomaly detection pipelines for real-time applications
  • Deployment platforms: AWS, Azure, GCP for scalable time series anomaly detection
  • Hands-on: Deploying an anomaly detection model to a cloud platform

Afternoon Session

• Real-World Use Cases of Anomaly Detection

  • Financial fraud detection, IoT monitoring, predictive maintenance, cybersecurity
  • Case studies and applications in different industries
  • Hands-on: Building a real-time anomaly detection dashboard for monitoring financial transactions

• Capstone Project & Final Presentations

  • Choose from:
    1. Detecting anomalies in e-commerce sales data
    2. Predictive maintenance for detecting sensor anomalies
    3. Real-time anomaly detection for IoT devices
  • Participants present their projects & receive expert feedback

• Certification & Networking Session

Post-Course Benefits

• Hands-on experience with real-world time series datasets and anomaly detection techniques
• Advanced skills in machine learning and deep learning methods for anomaly detection
• Expertise in deploying anomaly detection systems for real-time monitoring
• Portfolio-ready projects showcasing your anomaly detection capabilities
• Access to resources, datasets, and cloud deployment tools for future development