Explore the fundamentals of Time Series Analysis using the KNIME Analytics Platform in this comprehensive tutorial. Learn essential concepts including preprocessing, alignment, missing value imputation, forecasting, and evaluation. Build a practical demand prediction application using both (S)ARIMA models and machine learning approaches. Gain hands-on experience with KNIME's Time Series components for preprocessing, transforming, aggregating, forecasting, and inspecting time series data. Discover techniques for time aggregation, trend cycle analysis, and seasonality modeling. Dive into ARIMA modeling, autocorrelation, and model identification. Explore advanced topics like seasonal ARIMA and stream learning. Access example workflows to apply these concepts in your own projects.