AI for Time-Series Data: Analysis, Forecasting, and Anomaly Detection

Description

• Course ID:
  • IC-805-E-10
• Content: This course covers methods and applications of artificial intelligence for the analysis, modeling, and forecasting of time-series data in industrial and technical environments. The focus is on practical use cases such as predictive maintenance, forecasting models, and anomaly detection in real-world operational systems.
  • Key Topics:
    • Fundamentals of time-series data (structure, properties, challenges)
    • Data preprocessing for time series (noise, missing values, normalization)
    • Classical time-series analysis methods (trend, seasonality, autocorrelation)
    • Introduction to ML-based time-series models
    • Forecasting models for short-term and long-term predictions
    • Anomaly detection in time-series data
    • Predictive maintenance and condition monitoring
    • Feature engineering for time series (sliding windows, lag features, etc.)
    • Evaluation of time-series models (MAE, RMSE, MAPE, etc.)
    • Handling non-stationary data
    • Application of AI in industrial processes (manufacturing, energy, IoT)
    • Limitations and risks of AI-based forecasting systems
  • Practical Components:
    • Building a simple forecasting model for industrial time-series data
    • Developing an anomaly detection system based on sensor data
    • Analysis of a predictive maintenance use case
    • Comparison of classical vs. machine learning-based time-series methods
• Prerequisites:
  • Required:
    • IC-805-G-04 – Developing and Integrating AI Systems, or
    • Comparable knowledge in data analysis and machine learning
  • Recommended:
    • IC-805-G-06 – MLOps & AI Operations (for production deployment context)
    • Basic knowledge of statistics and Python-based data analysis
    • Not suitable for complete AI beginners without data or programming experience.
• Certification Requirement:
  • Without certification option
• Conduction Method:
  • Online (For on-site or in-house training, please send us a request via our contact form (Contact - intellcert). We will get in touch with you shortly thereafter.)
• Language:
  • English
• Target  Audience:
  • Data scientists and data analysts
  • Industrial engineers and automation specialists
  • AI developers focused on industrial applications
  • Professionals in manufacturing, energy, logistics, and IoT
  • Predictive maintenance and condition monitoring teams
  • Participants of IC-805-G-04 and IC-805-G-06
• Learning Objectives:
  • Participants will be able to:
    • Analyze and structure time-series data and identify common patterns (trend, seasonality, anomalies)
    • Apply AI models for forecasting and anomaly detection
    • Implement or evaluate predictive maintenance use cases
    • Select appropriate modeling approaches for time-series problems
    • Assess limitations of AI-based time-series systems in real-world environments
    • Understand industrial AI applications in monitoring and forecasting contexts
• Note:
  • Displayed price excl. VAT