nixtla-event-impact-modeler
Analyze causal impact of events on time series forecasts using TimeGPT. Use when quantifying promotion or disaster effects. Trigger with 'event impact analysis' or 'causal analysis'.
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Analyze causal impact of events on time series forecasts using TimeGPT. Use when quantifying promotion or disaster effects. Trigger with 'event impact analysis' or 'causal analysis'.
Forecasts orderbook depth and spreads to optimize trade execution timing. Use when needing to estimate market liquidity for large orders. Trigger with 'forecast liquidity', 'predict orderbook', 'estimate depth'...
Transforms forecasting experiments into production-ready inference pipelines with Airflow, Prefect, or cron orchestration. Generates ETL tasks, monitoring, error handling, and deployment configs. Activates when...
Fine-tunes TimeGPT on custom datasets to improve forecasting accuracy. Use when TimeGPT's zero-shot performance is insufficient or domain-specific accuracy is needed. Trigger with "finetune TimeGPT", "train Tim...
Detects anomalies in time series data using TimeGPT. Identifies outliers, level shifts, and trend breaks without model training. Use when identifying anomalies, outliers, or unusual patterns in time series. Tri...
Generate comprehensive pytest test suites from PRD functional requirements with fixtures, parameterization, and coverage tracking. Use when creating tests for new plugins, validating PRD requirements, or scaffo...
Generate production-ready Jupyter notebooks showcasing Nixtla forecasting workflows for statsforecast, mlforecast, and TimeGPT. Use when creating demos, building examples, or showcasing forecasting capabilities...
Quantifies prediction uncertainty using conformal prediction. Use when risk assessment, scenario planning, or decision-making under uncertainty is required. Trigger with "quantify uncertainty", "generate predic...
Analyze forecast uncertainty using conformal prediction. Use when risk assessment or scenario planning is required. Trigger with 'quantify uncertainty' or 'prediction intervals'.
Performs rigorous time series cross-validation using expanding and sliding windows. Use when needing to evaluate the performance of time series models on unseen data. Trigger with cross validate time series, ev...
Transform data sources to Nixtla schema (unique_id, ds, y) with column inference. Use when preparing data for forecasting. Trigger with 'map to Nixtla schema' or 'transform data'.
Automatically selects the best forecasting model between StatsForecast and TimeGPT based on time series data characteristics. Use when unsure which model performs best. Trigger with 'auto-select model', 'choose...
Detects anomalies in time series data using TimeGPT. Identifies outliers, level shifts, and trend breaks without model training. Use when identifying anomalies, outliers, or unusual patterns in time series. Tri...
Guides TimeGPT lab environment setup including Python dependencies, API key configuration, smoke testing, experiment workflows, and optional CI/CD integration. Inspects environment docs and scripts to provide s...
Analyzes Polymarket prediction market contracts and forecasts price movements using TimeGPT. Activates when analyzing prediction markets, forecasting contract outcomes, or evaluating market sentiment. Triggers...
Quantifies the impact of exogenous events on contract prices using TimeGPT and CausalImpact. Triggers on "event impact analysis", "model event effects", "quantify event impact", or "causal analysis".
Validate skills and plugins with deterministic evidence bundles and strict schema gates. Use when auditing changes or enforcing compliance. Trigger with 'run validation' or 'audit validators'.
Validate Nixtla skills and plugins with deterministic evidence bundles and strict schema gates. Use when auditing changes or enforcing compliance. Trigger with 'run validation' or 'audit validators'.
Assists users in migrating their codebase and data pipelines from TimeGPT-1 to TimeGPT-2. Use when upgrading to the latest version of TimeGPT, ensuring compatibility, and optimizing performance. Trigger with "m...
Detect arbitrage opportunities between Polymarket and Kalshi using forecast analysis. Use when finding price discrepancies across platforms. Trigger with 'find arbitrage' or 'compare market prices'.
Analyze Nixtla baseline forecasting results (sMAPE/MASE on M4 or other benchmark datasets). Use when the user asks about baseline performance, model comparisons, or metric interpretation for Nixtla time-series...
Generate production-ready plugin structures from PRD documents with enterprise-compliant files. Use when scaffolding new plugins, converting PRDs to plugin skeletons, or initializing plugin projects. Trigger wi...
Validate forecast quality by comparing MASE and sMAPE against benchmarks. Use when detecting model degradation. Trigger with 'validate forecast' or 'check forecast quality'.
Performs rigorous time series cross-validation using expanding and sliding windows. Use when needing to evaluate the performance of time series models on unseen data. Trigger with "cross validate time series",...
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.