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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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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Weak Supervision for Better Machine Learning Labels
Weak supervision helps teams scale labeling by combining imperfect rules, heuristics, and external signals instead of hand-labeling every example.
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Feature Engineering for Time Series Without Leaking the Future
Turning a time series into a tabular problem unlocks powerful models and introduces a specific failure: features that quietly contain information from the fu...
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Multiple Seasonality: MSTL, TBATS, and Fourier Terms
Hourly and daily data rarely has one season. Electricity demand cycles daily, weekly and annually at the same time, and a single seasonal period cannot repre...
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Multilevel Models for Operational Analytics
Multilevel models help analysts estimate group-level performance without overreacting to small samples or ignoring real differences between sites.
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Forecasting Baselines That Are Hard to Beat
An RMSE of 4.2 means nothing on its own. Without a baseline you cannot tell whether a model is skilful or merely arithmetic.
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