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State Space Models and the Kalman Filter
The Kalman filter is usually introduced as a tracking algorithm for spacecraft. It is more useful understood as the general engine for estimating hidden stat...
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Measurement Invariance for Machine Learning Monitoring
Learn how measurement invariance gives model monitoring teams a statistical language for detecting when features, labels, or scores stop meaning the same thi...
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A Glucose Spike Is Not a Diagnosis
Post-meal glucose excursions are part of normal physiology. Social media often treats any visible rise as metabolic damage, confusing a short-term biomarker ...
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Global vs Local Models in Time Series Forecasting
The traditional approach fits one model per series. Modern practice often fits a single model across thousands of them, and usually wins.
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Anomaly Detection in Time Series
Outlier detection asks whether a value is unusual. Time series anomaly detection asks whether it is unusual *now*, which is a different and harder question.
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Causal Feature Selection for Observational Machine Learning
Predictive feature selection is not enough when a model supports interventions. This article explains how causal thinking improves feature design in observat...
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Probabilistic Forecasting: Beyond the Point Estimate
A point forecast answers the wrong question. Most decisions depend on how bad things could plausibly get, which is a statement about the whole distribution.
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Missing Data and Irregular Sampling in Time Series
A missing row in a table is a nuisance. A missing interval in a time series changes the meaning of every lag, window and seasonal index computed from it.
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Conformal Prediction for Operational Risk Decisions
Conformal prediction helps teams express model uncertainty as calibrated intervals or prediction sets that can be used in operational risk decisions.
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Hierarchical Forecasting: Making Forecasts Add Up
Forecast every store separately and the total will not match the forecast you made for the company. Reconciliation is how you make a hierarchy of forecasts c...
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