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Time-series Modelling in Manufacturing and Process Industry


This webinar, presented by data scientist Dr Behzad Nobakht, will present a high-level overview of simple but key concepts in time-series modelling of industrial processes with the aim of maximising model performance and generalisability. These could have significant cost saving implications and the potential to increase plant operational efficiency and reduce downtime.

The webinar will be of interest to data scientists, machine learning engineers, sensor data analysts, manufacturing experts, and flow measurement engineers.

Our webinar will tackle these points, focusing on:

  • Multivariate time-series prediction problems
  • Correlations in time series data
  • Feature selection and extraction
  • Data reshaping
  • Data-driven models for time-series data
  • Model validation
  • Scalability

About the speaker

Dr Behzad Nobakht

Dr Behzad Nobakht is a data scientist at TÜV SÜD National Engineering Laboratory, currently working on data-driven strategies to develop Condition-Based Monitoring (CBM) solutions for use in industry.

He has a PhD in Petroleum Engineering from Heriot-Watt University. Before that he completed a research internship at Total E&P Pau, France. He also has a Master’s degree in Petroleum Engineering from Polytechnic University of Turin, and a Bachelor’s degree in Chemical Engineering from Sharif University of Technology.

Dr Nobakht is also responsible for automation of workflows using machine learning and advanced statistical methods for CBM modelling with realistic uncertainty quantification of model predictions.

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Middle East and Africa