Results for “predictions”

Computes the Anderson-Darling test statistic and p-value using scipy.stats.anderson for evaluating predictions against ground truth.

Queries Polymarket prediction market data via public REST APIs: markets, prices, orderbooks, and history.

Provides guidance on using the Aeon library for time series forecasting, covering model selection, implementation, and evaluation.

Computes Theil's U (uncertainty coefficient) between predictions and ground truth using the torchmetrics implementation, handling categorical data and NaN strategies.

Returns structured prediction-market data for Polymarket, Kalshi, Limitless, Opinion, Predict.Fun, dFlow, and UMA oracle via a local API, covering markets, cross-venue search, leaderboards, smart money, wallet analytics, identity clustering, and resolution status.

Kalshi prediction market data (prices, odds, orderbooks, trades). Use for prediction markets, Kalshi, betting odds, election and sports betting, market forecasts. Provides real-time market data, event series information, and comprehensive trading analytics.

Compares prediction market probabilities from Polymarket with options-implied probabilities to identify mispricing signals and potential arbitrage opportunities.

Build predictive models for sports and event outcomes using statistical methods, ELO ratings, regression, Monte Carlo simulation, and machine learning. Use when creating power rankings, projecting game outcomes, estimating win probabilities, or building a quantitative edge. Also trigger for 'prediction model', 'ELO rating', 'power rankings', 'win probability', 'Monte Carlo', 'regression model', 'expected goals', or 'predictive analytics'.

Predict continuous values from temporal sequences using aeon's time series regressors, covering convolutional, deep learning, distance-based, feature-based, hybrid, interval-based, and shapelet-based approaches.

Create Earth2Studio prognostic model wrappers that time-step weather forecasts forward, with triple-inheritance classes, tests, and documentation.

Evaluates the robustness and alignment with human judgment of reference-based and reference-free evaluation metrics for machine translation and summarization, particularly under adversarial conditions.

Build a quarterly bookings forecast with commit, best-case, and pipe-only tiers, project cohort-level NRR/GRR to surface leaky cohorts, and score per-stage funnel confidence using coefficient-of-variation analysis.

Analyzes earnings-season implied volatility: historical IV crush, implied move forecast, IV Rank strategy tag, and a priced Iron Condor quote ready to trade.

Monitors a watchlist of Kalshi prediction markets for price moves, volume spikes, resolution proximity, and kill-criterion triggers, with cross-venue arbitrage detection against paired Polymarket markets adjusted for fees and slippage.

Automate gender data lookups and name-based gender predictions using the Genderapi IO API through Rube MCP and Composio.

Computes the SQuAD metric using torchmetrics, given predictions and ground truth. Use when evaluating question-answering outputs with exact match and F1 scores.

Extract false-positive and false-negative gaps from VLM binary-classification-question predictions by comparing model responses against ground truth, producing a structured JSONL file and summary report for downstream root-cause analysis.

Use for writing, reviewing, debugging, testing, or diagnosing Bayesian regression and hierarchical models built with Bambi formulas, Model, Family/Likelihood/Link, Prior, fit, prior predictive, and predict. Trigger on common versus group-specific terms, categorical coding, family/link choice, automatic prior scaling, missing rows, PyMC backend settings, and InferenceData predictions. Do not use for hand-built PyMC graphs, NumPyro programs, ArviZ-only analysis of existing draws, frequentist statsmodels formulas, or generic pandas work.