No-Change Forecast Algorithm

A No-Change Forecast Algorithm is a baseline forecast algorithm that predicts zero change from current state for low-probability events.

• AKA: Persistence Forecast Algorithm, Status Quo Prediction Algorithm, Zero-Change Baseline, Naive No-Change Model.
• Context:
  • It can typically predict no-change forecast algorithm outcomes for rare events.
  • It can typically serve as no-change forecast algorithm benchmark for human forecaster performance.
  • It can typically provide no-change forecast algorithm baselines for model comparison.
  • It can often outperform no-change forecast algorithm competitors on stable variables.
  • It can often fail on no-change forecast algorithm weaknesses like trending variables.
  • It can often combine with no-change forecast algorithm ensembles for improved accuracy.
  • It can range from being a Simple No-Change Forecast Algorithm to being a Adjusted No-Change Forecast Algorithm, depending on its domain tuning.
  • It can range from being a Binary No-Change Forecast Algorithm to being a Continuous No-Change Forecast Algorithm, depending on its variable type.
  • It can range from being a Single-Variable No-Change Forecast Algorithm to being a Multi-Variable No-Change Forecast Algorithm, depending on its input dimension.
  • It can range from being a Static No-Change Forecast Algorithm to being a Adaptive No-Change Forecast Algorithm, depending on its update mechanism.
  • ...
• Examples:
  • Tournament No-Change Forecast Algorithms, such as:
    • XPT No-Change Baseline, for existential risk questions.
    • GJO No-Change Benchmark, for geopolitical events.
  • Domain-Specific No-Change Forecast Algorithms, such as:
    • Weather Persistence Model, for short-term forecasts.
    • Economic Status Quo Model, for stable indicators.
  • Hybrid No-Change Forecast Algorithms, such as:
    • Threshold No-Change Model, with activation conditions.
  • ...
• Counter-Examples:
  • Trend Extrapolation Forecast Algorithm, which incorporates historical trends.
  • ARIMA Algorithm, which models time series dynamics.
  • Machine Learning Forecast Algorithm, which learns complex patterns.
• See: Baseline Forecast Algorithm, Persistence Model, Naive Forecast Method, Trend Extrapolation Forecast Algorithm, Forecast Error Measure, Forecasting Performance Measure, Statistical Baseline, Time Series Forecasting Algorithm, Forecast Evaluation Framework.

---