3. Data, Science & AI

Automatically selects the optimal forecasting model (StatsForecast or TimeGPT) based on time series data characteristics for maximum accuracy.

Quantifies forecast uncertainty using conformal prediction for risk assessment and scenario planning, providing prediction intervals.

Analyzes co-activation patterns, shared drivers, and subsystem relationships in SAE feature data through data-driven methods.

Analyzes multi-asset correlations and provides hedge recommendations to mitigate portfolio risk.

Converts experiment results into structured Markdown research notes and optionally updates research state with evidence for scientific documentation.

Validates time series forecast quality using MASE and sMAPE metrics against benchmarks to detect model degradation.

Automatically selects the optimal forecasting model (StatsForecast or TimeGPT) for time series data, eliminating manual model choice.

Configures TimeGPT fine-tuning for domain-specific time series forecasting models using Nixtla SDK.

Fine-tunes TimeGPT on custom datasets to enhance domain-specific forecasting accuracy beyond zero-shot performance.

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

Matches Sparse Autoencoder (SAE) features between Ultra (24K) and Full (2K) language models using activation patterns and token overlap.

Analyzes SAE decoder weights to determine output influence, feature importance, and decoder similarity for model interpretability.

Forecasts Polymarket contract price movements using TimeGPT AI model for market sentiment and outcome analysis.

Analyzes Nixtla baseline forecasting results, interpreting sMAPE/MASE metrics for model comparisons on benchmark datasets.

Generates Nixtla-compatible schema transformations by inferring column mappings and validating data quality for CSV, SQL, and Parquet sources.

Detects arbitrage opportunities between Polymarket and Kalshi using forecast analysis for price discrepancy identification.

Provides a quick overview of SAE (Sparse Autoencoders) features including top tokens and activation statistics.

Calculates market risk metrics including Value at Risk (VaR), volatility, drawdown, and position sizing for portfolio management.

CLI tool for generating and configuring Nixtla Skills to set up forecasting workflows with ease.

Generates production-ready Jupyter notebooks for Nixtla forecasting demos using statsforecast, mlforecast, and TimeGPT.

Generates time series forecasts using TimeGPT, StatsForecast, and MLForecast for demand planning and model comparison.

Generates markdown benchmark reports for forecast model accuracy, including model comparisons and statistical analysis to validate performance.

Analyzes multi-contract correlations to generate forecast-based hedge recommendations for financial risk management.

Integrates external variables (holidays, weather, events) into TimeGPT forecasts to improve demand and sales prediction accuracy.