Temporal and Spatial Data Mining R2a


Credits points: 6

60 hours course attendance; 120 hours self-study

Semester: winter

Language: English

Module type: elective

Module usability: M.Sc. Electrical Communication Engineering, M.Sc. Elektrotechnik

Module duration: one semester

Required qualifications:
At least one Bachelor or Master module in machine learning should have been attended, knowledge gaps can be closed in online courses on machine learning

Basic knowledge of stochastic, analysis and linear algebra is assumed

Additional, Python knowledge is beneficial

Competences to be acquired

Develop new modeling approaches for problems such as time series classification, anomaly detection, or clustering

Plan and implement new applications of the learned paradigms

Critically question, compare, and evaluate existing approaches and applications



  • Basic approaches of pattern recognition in time series (e.g., sensor signals) and spatially distributed data (e.g., in sensor networks)
  • Theoretical foundations (e.g., segmentation of time series, correlation of data)
  • Time series representation (e.g., features extraction for describing temporal and spatial data)
  • Distance and similarity measures for time series, clustering / classification, motifs, and anomaly/novelty detection using various techniques (e.g., nearest neighbor, neural networks, support vector regression)
  • Diverse sample applications (signature verification, collaborative hazard warning for automotive, activity recognition, etc.)

Learning outcomes

  • Explain various tasks, models, and algorithms of temporal and spatial data mining


  • Lecturer: Bernhard Sick and team
  • Teaching method: lecture
  • SWS: 4
  • Credit points: 6
  • Examination: oral exam (20 minutes) or written exam (120 minutes)
  • Course identifier: FB16-6974