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dataentity · one source so far

glmmtmb

for R statisticians, data scientists

Steadystackoverflow
Signal score
19
Live items
1
Trajectory
→ Steady
7-day est. ~25

Why this scored 19

every term, weighted
Velocity+0.0 / 40

Its fastest-moving item, measured against the pace of its own source

Acceleration+11.5 / 25

Whether that velocity is itself speeding up, as a per-hour rate

Cross-source spread+0.0 / 25

How many independent communities its own items come from

Recency+7.8 / 10

Decays to zero over 14 days, counted from when we first saw it

Saturation penalty−0.7 / 30

Subtracted once something is big and old — sized by its biggest item, aged from when we first saw it

Composite18.7

Weights are hand-tuned, not learned — we're calibrating them against realized trends as history accumulates. On an entity's first sighting there's no previous reading to compare against, so acceleration starts from a neutral prior rather than a measurement, and velocity falls back to engagement over its whole lifetime until a second reading exists. Full methodology

Outlook

low confidence · estimate, not a guarantee

7-day

~25

range 0–37

14-day

~25

range 0–40

30-day

~26

range 0–45

Signal history

7-day window (free)
1758

projected trajectory (estimate, not a guarantee)

The evidence

The live items this entity's score aggregates — every community independently talking about it right now. This is the corroboration, shown, not claimed.

  1. 1
    18

    How to code temporal auto-correlation for irregular-spaced observational data in glmmTMB?

    Shows how to add temporal autocorrelation for irregular data in glmmTMB. · for R statisticians, data scientists

    stackoverflowSteadydata3d ago