Statistical Weather Signals For Prediction Markets
LantzNet PolyWeather Signals compares multiple forecast models, observed weather history, and live market conditions to identify statistically favorable YES and NO temperature opportunities.
Use the mobile dashboard for a cleaner phone-friendly view of city signals, YES/NO opportunities, and temperature ranges.
What We Do
PolyWeather Signals organizes forecast, observed weather, and market data into readable temperature-market signals. The platform is designed to help users compare forecast probability against market pricing without relying on a single weather source.
Forecast Data Stack
LantzNet does not rely on a single weather app forecast. Each supported city is analyzed using forecast model guidance, observed weather history, raw forecast records, and forecast-to-market matching data.
Broad global forecast model guidance used for temperature range analysis.
Regional North American model guidance used for short-range temperature comparison.
Medium-range forecast guidance used as an additional independent model layer.
Additional meteorological data layer used for weather context and comparison.
Historical actual temperatures used to compare forecasts against real outcomes.
Structured forecast data used to support city-level temperature analysis.
Forecast data aligned to Polymarket-style temperature conditions and YES/NO ranges.
Signal Intelligence
LantzNet combines model forecasts, observed weather, raw forecast records, and market-condition matching to produce statistical temperature signals for prediction-market research.
Compare GFS, NAM, ECMWF, NASA POWER, and observed weather instead of relying on one forecast.
Align forecast data to Polymarket temperature conditions and YES/NO ranges.
Display confidence, source agreement, and potential edge without promising outcomes.
City Coverage
PolyWeather Signals currently focuses on major temperature-market cities and compares each location across multiple forecast and observed-weather layers.
Methodology
A plain-English process for turning weather and market data into temperature-market signals.
Gather forecast guidance, observed weather, and market data.
Convert inputs into comparable city-level temperature ranges.
Evaluate whether sources cluster around the same outcome or disagree.
Align forecast probabilities with YES and NO temperature ranges.
Highlight where forecast probability and market pricing appear meaningfully different.
Why It Matters
| Capability | LantzNet PolyWeather Signals | Generic Weather Apps |
|---|---|---|
| Source View | Multi-source forecast comparison | General consumer forecast |
| Probability View | Temperature-range probabilities | Point forecasts and summaries |
| Market Context | Forecast probability compared with market pricing | No market-pricing context |
| Decision Support | YES/NO signal framing with risk context | General weather planning |
Simplified Probability Distribution
Visual clarity for confidence zones and tail-risk interpretation, supporting faster decision cycles when markets shift.
Dashboard Preview
The dashboard combines range probabilities, YES/NO outcomes, edge analysis, and live comparisons in one readable interface tuned for fast interpretation.
LantzNet
Sample display only — not live signal data.
| Temp Range | Fcst | NO | Risk | Edge | Conf |
|---|---|---|---|---|---|
| 74-75°F | 96.7% | 91¢ | Safe | +5.7 pp | 45% |
| 76-77°F | 81.3% | 76¢ | Safe | +5.3 pp | 45% |
| 72-73°F | 99.8% | 97¢ | Safe | +2.7 pp | 45% |
| 82-83°F | 88.8% | 91¢ | Safe | -2.2 pp | 45% |
Pulled from current dashboard structure: Temperature Range, Forecast Probability, NO Price, Expected Risk, Edge, Confidence.