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Exploring the frontiers of AI, software architecture, and engineering.
Finding the Rhythm: Seasonality Detection in Business Data
Sales dip every Sunday. Traffic peaks on Wednesdays. If you do not model the rhythm, your forecasts and anomaly alerts will keep crying wolf — here is how to detect cycles properly.
Read Article →What a Confidence Interval Actually Means in Forecasting
A forecast without error bars is a guess wearing a suit. But most forecast bands promise more than they deliver — here is what a 95% interval actually means and how to keep it honest.
Read Article →Grading Your Data: A Practical Data-Quality Scorecard
A data-quality grade turns vague unease about your spreadsheet into a number you can act on. Here is the measurement framework: completeness, uniqueness and validity, scored per column.
Read Article →Beyond Counting: What Categorical Columns Actually Say
Counts of categories hide the interesting part: concentration. A Herfindahl index and a top-share number reveal whether one category dominates or everything is fragmented.
Read Article →The Floating-Point Problem: Why Your Sums Are Wrong
0.1 + 0.2 is not 0.3 in any language. Floating-point arithmetic drifts, cancellation destroys precision, and your grand totals quietly inherit the damage. Here is how it works and what to do.
Read Article →Measuring Your Forecast: MAPE, RMSE, and the Holdout
A forecast is only as good as its error metric — and every popular error metric lies in a different way. Here is how to evaluate forecasts with a holdout and pick the right measure.
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