Applying Propositional Learning to Time Series Prediction
Contents

• 1. Introduction
• 2. Adapting ML Systems to Prediction Problems
  • 2.1 Representing a time series in a propositional language
  • 2.2 Time dependencies
    • 2.2.1 Introducing New Attributes
    • 2.2.2 Selective Attribute Introduction
• 3. Experiments
  • 3.1 The data sets
  • 3.2 Used learning engine
  • 3.3 The experiments
• 4. Conclusions