Book Content
chapters • 13h4m total length
1. Introducing Time Series Analysis
2. Introduction to KNIME Analytics Platform
3. Preparing Data for Time Series Analysis
4. Time Series Visualization
5. Time Series Components and Statistical Properties
6. Humidity Forecasting with Classical Methods
7. Forecasting the Temperature with ARIMA and SARIMA Models
8. Audio Signal Classification with an FFT and a Gradient Boosted Forest
9. Training and Deploying a Neural Network to Predict Glucose Levels
10. Predicting Energy Demand with an LSTM Model
11. Anomaly Detection – Predicting Failure with No Failure Examples
12. Predicting Taxi Demand on the Spark Platform
13. GPU Accelerated Model for Multivariate Forecasting
14. Combining KNIME and H2O to Predict Stock Prices
