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vignesh2027
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timeseries-oracle

Forecast time series data with uncertainty quantification and prediction intervals.

6|Updated May 20, 2026
Search Tags:#forecasting#time-series#model-selection#anomaly-detection#scenario-planning#uncertainty-quantification#prediction-intervals
One-click install
npx skills add https://github.com/vignesh2027/Claude-Agentic-Skills2.0-version --skill timeseries-oracle
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: timeseries-oracle
Source: https://github.com/vignesh2027/Claude-Agentic-Skills2.0-version/tree/main/timeseries-oracle
Command: npx skills add https://github.com/vignesh2027/Claude-Agentic-Skills2.0-version --skill timeseries-oracle

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

TimeSeriesOracle provides production-grade forecasting for time series with uncertainty quantification, enabling better planning, anomaly detection, and scenario analysis.

Core Features & Use Cases

  • Decomposition-first forecasting to separate trend, seasonality, and residuals.
  • Model selection guidance for Prophet, ARIMA/SARIMA, VAR, LightGBM, and LSTM, plus ensemble approaches.
  • Anomaly detection, prediction intervals, and scenario planning for Base/Bull/Bear projections.

Quick Start

Load your time series data and initialize TimeSeriesOracle to start forecasting with uncertainty quantification.

Frequently Asked Questions about timeseries-oracle

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I forecast time series data with uncertainty quantification?▼

To forecast time series with uncertainty, use a decomposition-first workflow to separate trend, seasonality, and residuals, then generate prediction intervals for robust projections.

What is the best way to compare ARIMA, Prophet, and LSTM for time series forecasting?▼

For time series model selection, compare ARIMA, Prophet, and LSTM by evaluating their prediction intervals and decomposition fit to determine the best approach for your specific data.

Can I use time series forecasting for scenario planning and anomaly detection?▼

Yes, time series forecasting supports anomaly detection and scenario planning by generating Base, Bull, and Bear projections with quantified prediction intervals to identify outliers.

Does time series forecasting support multivariate models like VAR?▼

Yes, time series forecasting supports multivariate models including VAR, alongside LightGBM and LSTM, enabling complex trend and seasonality decomposition with prediction intervals.

When do I need prediction intervals for time series forecasting?▼

You need prediction intervals for time series forecasting when conducting scenario planning or anomaly detection, as they quantify uncertainty to ensure robust operational and financial projections.

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