machine learning

forecasting errors are usually not brought on by bad time-series models. They're brought on by ignoring structure. SKUs don't behave independently. They interact through shared plants, product groups, warehouses, and storage locations. A requirement shock...

, the thought has circulated within the AI field that prompt engineering is dead, or not less than obsolete. This, on one side because pure language models have turn out to be more flexible...

Introduction undefined, I began eager about the parallels between point-anomaly detection and trend-detection. In relation to points, it’s generally intuitive, and the z-score solves most problems. What took me some time to determine was applying...

AM on a Tuesday (well, technically Wednesday, I suppose), when my phone buzzed with that familiar, dreaded PagerDuty notification. I didn’t even must open my laptop to know that the daily_ingest.py script had failed....

. What a present to society that is. If not for google trends, how would we've ever known that more Disney movies released within the 2000s led to fewer divorces within the UK. Or that drinking...

in supply-chain planning has traditionally been treated as a time-series problem. Each SKU is modeled independently. A rolling time window (say, last 14 days) is used to predict tomorrow’s sales. Seasonality is captured, promotions are added,...

What motivates you to take dense academic concepts (like Stochastic Differential Equations) and switch them into accessible tutorials for the broader TDS community? It’s natural to wish to learn all the pieces in its natural...

of birds in flight. There’s no leader. No central command. Each bird aligns with its neighbors—matching direction, adjusting speed, maintaining coherence through purely local coordination. The result's global order emerging from local consistency. Now imagine...