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npx skills add https://github.com/pluginagentmarketplace/custom-plugin-ai-data-scientist --skill time-series
Please help me install this Agent Skill. Skill: time-series Source: https://github.com/pluginagentmarketplace/custom-plugin-ai-data-scientist/tree/main/skills/time-series Command: npx skills add https://github.com/pluginagentmarketplace/custom-plugin-ai-data-scientist --skill time-series
Temporal data is often volatile and difficult to forecast; this skill provides a structured framework to analyze patterns, test stationarity, and build accurate forecasting models across multiple methodologies (ARIMA, SARIMA, Prophet, and exponential smoothing) to support demand forecasting, trend detection, and anomaly identification.
Prepare a time-series dataset and run the time-series skill to generate forecasts for the next 30 periods.
High-intent search queries and answers about installing and using this skill.
You can forecast time-series data by applying ARIMA, SARIMA, Prophet, or exponential smoothing models. This skill auto-selects the best methodology, handles data preparation, performs feature engineering, and evaluates models to generate accurate temporal predictions.
Detecting anomalies in temporal data involves analyzing historical patterns, seasonality, and trends. This skill identifies outliers by monitoring deviations from expected values across finance, retail, and operations domains to support demand planning.
Testing stationarity is required before using SARIMA for seasonality analysis. This skill provides a structured framework to test stationarity, analyze temporal patterns, and build accurate forecasting models for robust demand planning.
Yes, you can use exponential smoothing for demand forecasting in operations. This skill supports exponential smoothing alongside ARIMA and Prophet to monitor trends, analyze seasonality, and inform business planning decisions.
Preparing a dataset for time-series forecasting requires structuring temporal data and engineering features. This skill supports data preparation, feature engineering, model evaluation, and visualization to generate forecasts for upcoming periods.
KaranKathur06Convert raw tabular and time-series data into modeling-ready datasets with statistical analysis.
Arnutt-NConsolidate real-time HR-IMS audit events into a unified monitoring dashboard.
vignesh2027Forecast time series data with uncertainty quantification and prediction intervals.
ovachieverEstimate regression and time-series models for statistical inference.
