Abstract: 

This course introduces the main concepts behind Time Series Analysis, with an emphasis on forecasting applications: data cleaning, missing value imputation, time-based aggregation techniques, creation of a vector/tensor of past values, descriptive analysis, model training (from simple basic models to more complex statistics and machine learning based models), hyperparameter optimization, and model evaluation.
Learn how to implement all these steps using real-world time series datasets. Put what you’ve learnt into practice with the hands-on exercises.

Session Outline
Session 1: Introduction to Time Series Analysis and KNIME Components
Session 2: Understanding Stationarity, Trend and Seasonality
Session 3: Naive Method, ARIMA models, Residual Analysis
Session 4: Machine Learning, Model Optimization, Deployment
Session 5: Recap and final Q&A

Bio: 

Maarit Widmann is a data scientist at KNIME. She started with quantitative sociology and she holds her Bachelors degree in Social Sciences. The University of Konstanz made her drop the “social” as a Master of Science. Her ambition is to communicate concepts behind data science to others in videos and blog posts.