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 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: Corey Weisinger is a Data Scientist with KNIME in Austin Texas. He studied Mathematics at Michigan State University focusing on Actuarial Techniques and Functional Analysis. Before coming to work for KNIME he worked as an Analytics Consultant for the Auto Industry in Detroit Michigan. He currently focuses on Signal Processing and Numeric Prediction techniques and is the Author of the Alteryx to KNIME guidebook.