Chapter 9: Model-Based Feature Extraction

Uncertainty as a Feature advanced

Why two identical forecasts can imply very different decisions once you look at what the model does not know.

Why two identical forecasts can imply very different decisions once you look at what the model does not know.

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References

Making and Evaluating Point Forecasts

Tilmann Gneiting — Journal of the American Statistical Association

Machine Learning for Probabilistic Prediction (PhD thesis, VALERY MANOKHIN)

Valery Manokhin (2022)

Chapter

9 Model-Based Feature Extraction

More Topics

Autoregressive, Moving-Average, and ARIMA Foundations for Feature Engineering Bayesian Inference and MCMC for Time Series Fractional Differencing: Keeping Memory Without Keeping the Unit Root Path Signatures and Log-Signatures for Financial Sequences Regime Models for Feature Engineering: HMMs, Markov Switching, and Distributional Clustering State-Space Models and Kalman Filtering for Feature Engineering Stationarity Tests: ADF, KPSS, and Rolling Stability Signals Structural Break Diagnostics and Time-Since-Break Features Volatility Models as Feature Extractors: GARCH, EGARCH, and HAR Wavelets for Multi-Scale Diagnostics and Causal Feature Design