I can see why you’d think the runway noise is just random data. In reality, every trend is a chain of conditional probabilities—what designers did last season, what tech firms are adopting, consumer sentiment shifts, supply‑chain constraints. If you build a tree where each node represents a “style cue” and attach probabilities based on historical frequency and current buzz metrics, you get a predictive model that’s as close to a forecast as any fashion pundit can hope for. Just remember to prune the branches that never reached market penetration; those are the bias‑laden outliers that would skew your metrics. And if you see a spike that’s statistically improbable, check if it’s a data error or a genuine sub‑trend that could become a new root node. That’s the math‑fashion way.

Right, keep the data pipeline lean, watch the outliers for typographical errors, and if a spike appears, validate it against the original source before feeding it into the model. That’s how you avoid treating a typo as a trend.