Time Series Analysis and Forecasting with RapidMiner and RapidAnalytics
This training course provides a compact two day introduction into the foundations of statistical learning for forecasting / predictions. The analysis of data changing over time or the prediction of future time points (forecasting) are among the most important tasks for data analysts. A successful solution often means a great business value. The topics of this course include necessary preprocessing steps for numerical data transformations, an introduction into statistical regression methods, neural networks, and support vector machines (SVM), and a discussion of validation methods in order to measure the goodness of the predictions. Many practical exercises will be performed with the data mining suite RapidMiner. This ensures that the participants will be able to quickly transfer the gained knowledge to own prediction problems.
After the training course the participants will have the ability to
- identify optimal processes for time series analysis,
- create forecasts for given data,
- transform series in a structured format suitable for modeling,
- apply regression methods like neural networks or support vector machines,
- visualize series data and the corresponding predictions.
This training course together with the course “Data Mining / Predictive Analytics with RapidMiner and RapidAnalytics 4” is the recommended preparation for the exam for becoming a certified Rapid-I Master.
- Course ID: 106
- Number of days: 2 days
- Location: Dortmund, Germany
- Target audience: users, analysts, developers, administrators
- Previous knowledge: good knowledge of data mining and RapidMiner
- Methods: lectures, discussions, individual and group work, exercises on realistic data.
Participants may introduce own work and project specific questions in order to find particular solutions together with the trainer and otherparticipants. The training course addresses intermediate learners and we recommend to visit the courses “Data Mining / Predictive Analytics with RapidMiner and RapidAnalytics 1” and “Data Mining / Predictive Analytics with RapidMiner and RapidAnalytics 2” before visiting this one.
Topics: The topics include:
- Basic concepts
- Definition of forecasting of series data
- Transforming a series to a structured data format
- Time series analysis and forecasting
- Influence of the prediction horizon
- Handling missing series values
- Advanced modeling
- Regression methods
- Linear Regression on Windows
- Neural Networks
- Regression with Support Vector Machines
- Validation of forecasting
- Performance criteria for forecasting
- Estimation schemes for forecasting
- Visualization of series data and predictions
- Visualizations for width format
- Visualizations for long format
- Visualizing predictions
- Feature extraction
Transforming the series
Extracting describing features
Commonly used features
|Number of Participants:
||4 or more
|Price per Participant:
Value added tax (VAT) may have to be added to these prices.