General concepts 

• Sampling theorem
• Time windows and aggregation
• Data splitting for time series data
• Common data quality problems and how to solve them
• Aligning flow by using dynamic time warping
• Digital Twins 

Pre-processing sensor data time series 

• Interpolation
• Noise reduction 
• Outlier detection
• Dimensionality reduction
• Dealing with mixed sample frequencies 

Feature calculation 

• Why?
• Autocorrelation
• Fourier transformations
• Peak detection
• ARIMA models

Forecasting  (on demand)

• Arima models
• Exponential smoothing models
• Anomaly detection based on forecasting 

Predictive maintenance (on demand)

• Predictive maintenance and survival function estimation 
• Kaplan Meier estimators
• Cox Proportional Hazard model
• Aalen Additive Hazard model
• Time series similarity kernel regression 

Deep Learning for sensor data (on demand)

• Recurrent architectures
• Convolutional architectures
• Anomaly detection based on auto-encoders
• Reinforcement learning for optimal control
• Differential evolution
• Surrogate/Bayesian optimization 

Case studies and exercises will take place per chapter.

The course outline will be tailored to the participant’s wishes and needs.