Calibration of YouTube View Forecast Model

Subject: Quote request. Probabilistic calibration of a forecasting model for expected-value calculation Hello, I am looking for a statistician or a small specialised firm for a fixed-price engagement. I would like a single quote for the whole work. GOAL Correctly calculate the expected value of bets on prediction markets. Expected value comes from comparing the estimated probability of each outcome range with its market price: if that probability is poorly calibrated, expected value is wrong even when the central estimate is accurate. Calibration is therefore the core of the engagement. I am not asking for a certain prediction of the outcome, but for probabilities calibrated as well as the available data allows, with their uncertainty measured and stated. CONTEXT The system forecasts the view counts of videos from a large YouTube channel from 24 hours to one week after publication. Markets pay out by view-count range, with day-1, day-2 through day-6 and week-1 horizons. At each point in time the model outputs a mean μ, a standard deviation σ and a probability for each range. MODEL Forecasts are routed by how long the video has been observed: - day-1: LightGBM trained on our own 2-minute series, 24h target; - week-1 before 48h: the 24h forecast times an empirical ratio measured on 3 videos; - from 48h: a decay LightGBM trained on public Kaggle data; - below 2h no model is considered reliable. LightGBM gives point estimates: σ is estimated from residuals by observation age, with a floor that grows with the share of the horizon still unobserved. Range probabilities are derived from μ and σ. I am looking for experience with uncertainty and calibration on gradient boosting models: quantile regression, conformal prediction, probability calibration, Brier score and log-loss. DATA AND COLLECTION - Collection starts automatically within minutes of a new video being published and stops exactly 168 hours later. - View counts are recorded from the fifth minute after publication up to the market horizon, every 40 seconds to 2 minutes. - Market order books for every range every 2 minutes, plus executed trades and settled outcomes. - Range thresholds already verified against the original market questions. - Complete videos collected with the current pipeline: 4, from 25 July to 5 September 2026. Earlier history is available but often incomplete. The 2-minute observations are strongly autocorrelated: the unit of analysis is the event, not the individual sample. CODE The pipeline is frozen at 13 July 2026 under a reference tag and has not been modified since. Read access to code and data will be provided, under an NDA if preferred. WORK REQUESTED 1. Criteria fixed in writing before seeing any results: value at the horizon, metrics and thresholds, on the frozen code version. 2. Audit: bias of μ, coverage of σ and calibration of the probabilities using reliability, Brier and log-loss, against a naive forecaster and a uniform distribution. Results stratified by observation time, with attention to 0-2h and 2-6h, where errors are largest. 3. Calibration: recalibration of μ, σ and the range probabilities, with numerical parameters that can be applied to the model, reported in full. 4. Impact on expected value: how much the remaining probability error translates into expected-value error, by observation-time band. 5. Final report with an outcome (pass, fail, insufficient data), limitations, uncertainty and a procedure repeatable on future videos. TIMELINE There is no fixed end date. Collection continues with every new video, roughly one every 14 days, and the project ends when collection ends. It is up to you to decide when the data is enough. OUT OF SCOPE Software development, infrastructure, integration with the execution system. Payment is tied to delivery of the work, not to betting results. IN YOUR QUOTE, PLEASE INCLUDE - a single fixed price, possibly with payment milestones; - how many complete videos you consider necessary, and why; - whether and how the price changes with the number of videos; - what you can deliver right away on the current data; - examples of work on probability calibration or probabilistic forecasting. Kind regards