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Fourier Analysis for Data Science: From Signals to Features
Fourier analysis is more than a signal-processing trick. It is a way to ask which cycles, rhythms, and scales explain variation in data.
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Counterfactual Evaluation for Decision Policies
Counterfactual evaluation helps teams estimate how a new decision policy might perform before deploying it to users, patients, customers, or operations.
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Anomaly Detection in Sensor Streams
Sensor anomaly detection works best when statistical signals, domain constraints, and alert workflows are designed together.
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Prevalence Shift and Base-Rate Drift in Machine Learning
Prevalence shift occurs when the base rate of the outcome changes, breaking thresholds, workloads, and probability interpretation even when the model ranking...
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Uplift Modeling for Targeted Interventions
Uplift modeling estimates treatment effect heterogeneity so interventions can target the people, assets, or cases most likely to benefit.
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Forecast Combination: Why Averaging Usually Wins
Choosing the best model is the obvious strategy. Averaging several is usually better, and the reason is not that the average is smarter but that it is less w...
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Regime-Switching Models for Time Series
A single model fitted across a recession and an expansion describes neither. Regime-switching models allow the dynamics themselves to change, with the regime...
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The Largest Eigenvalue of Noise Is Not Evidence of a Factor
When the number of variables is not small relative to the sample size, sample covariance eigenvalues spread dramatically even if the true covariance is the i...
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Nowcasting with Mixed-Frequency Data
The quantity you care about arrives quarterly and two months late. Related indicators arrive daily. Nowcasting is the problem of estimating the present from ...
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Interrupted Time Series and Causal Impact
A intervention happened at a known date and you need its effect. There is no control group, only the series itself before and after, and the counterfactual h...
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