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.
This course consists of four, 90 minutes online sessions run by Professor Daniele Tonini and two of our KNIME data scientists. Each session has an exercise for you to complete at home. The course concludes with a 15 to 30 minute wrap up session.
- 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
Prof. Daniele Tonini (University of Bocconi)