3. Data, Science & AI

Generates plain English explanations and executive summaries of TimeGPT forecast results, including driver analysis for stakeholders and compliance documentation.

Generates production-ready time series forecasting experiments using StatsForecast and TimeGPT for model benchmarking and cross-validation.

Provides a categorized list of installed Nixtla Skills for time series forecasting guidance, enabling users to select optimal forecasting tools.

Transforms prediction market data into Nixtla's required format with schema mapping and type validation for forecasting tools.

Automates parallel time series forecasting for multiple contracts using TimeGPT API, optimizing throughput with rate limiting and portfolio aggregation.

Forecasts Polymarket contract prices using TimeGPT with confidence intervals for market analysis.

Provides time series forecasting and analysis using TimeGPT, StatsForecast, and MLForecast for sales, demand, and revenue prediction.

Automates parallel time series forecasting for multiple contracts using TimeGPT, optimizing efficiency for large-scale data processing.

Analyzes and interprets baseline forecasting performance metrics (sMAPE, MASE) for Nixtla time-series models on benchmark datasets, including model comparisons.

Detects time series anomalies including outliers, level shifts, and trend breaks using TimeGPT without model training.

Manages feature labeling workflow including queue management, storage, and progress tracking for machine learning data preparation.

Proposes next steps for mechanistic interpretability experiments by analyzing current research, hypotheses, and evidence in AI model understanding.

Optimizes cost for Nixtla library usage by auditing TimeGPT, StatsForecast, and MLForecast patterns and suggesting efficient routing strategies.

Generates benchmarking pipelines to compare forecasting models (TimeGPT, StatsForecast, etc.) and summarize accuracy-speed trade-offs for model selection.

Analyzes market risk using Value at Risk (VaR), volatility metrics, and position sizing based on forecasted financial data.

Produces verified datasets, evaluation results, and deployable contract bundles to ensure data and evaluation correctness in workflows.

Forecasts orderbook depth and spreads to optimize trade execution timing for large orders.

Analyzes causal impact of events (e.g., promotions, disasters) on time series forecasts using TimeGPT AI model.

Scaffolds production-ready forecasting experiments with Nixtla libraries, including configuration, model comparison, and cross-validation workflows.

Validates feature interpretations in ML models using split-half stability and shuffle null tests for credibility assessment.

Quantifies exogenous event impacts on contract prices using TimeGPT and CausalImpact for causal analysis.

Transforms data sources into Nixtla's required schema (unique_id, ds, y) for time series forecasting preparation.

Quantifies prediction uncertainty in machine learning models using conformal prediction for risk assessment and decision-making.

Converts prediction market data to Nixtla's required schema (unique_id, ds, y) for time series forecasting model input.