Online Sequential Prediction via Incremental Parsing: The Active LeZi Algorithm

Intelligent systems that can predict future events can make more reliable decisions. Active LeZi, a sequential prediction algorithm, can reason about the future in stochastic domains without domain-specific knowledge. In this article, potential of constructing a prediction algorithm based on data compression techniques are investigated. Active LeZi prediction algorithm approaches sequential prediction from an information-theoretic standpoint. For any sequence of events that can be modeled as a stochastic process, ALZ uses Markov models to optimally predict the next symbol

Authors

• Karthik Gopalratnam Henry Kautz
• Diane Cook

Keywords

• Predictive models
• Prediction algorithms
• Stochastic processes
• Compression algorithms
• Intelligent systems
• Data compression
• Frequency
• Probability
• Information theory
• Entropy
• Incremental Parsing
• Sequence Of Events
• Sequence Prediction
• Optimal Prediction
• Symbol Sequence
• Compression Algorithm
• Entropy Rate
• Smart Environment
• Entropy Source
• Model In Order
• Alphabet
• Time Distribution
• Input Sequence
• Tree Nodes
• Prediction Time
• State Machine
• Null Results
• Smart Home
• Probability 2
• Tree Depth
• Input Symbols
• Universal Predictor
• Concept Drift
• Percent Accuracy
• Substring
• Queue Length
• User-defined Parameters
• Context Prediction
• Slow Convergence Rate
• sequential prediction
• smart environments
• Active LeZi
• MavHome