time-series

Name: time-series
Availability: InStock
Author: plugin

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4|1|Updated Nov 18, 2025

Search Tags:#forecasting #time-series #anomaly-detection #feature-engineering #arima #sarima #prophet

Ready to run

npx skills add https://github.com/pluginagentmarketplace/custom-plugin-ai-data-scientist --skill time-series

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires yaml, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

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.

Core Features & Use Cases

Auto-select and apply ARIMA/SARIMA/Prophet models for forecasting and seasonality analysis.
Support data preparation, feature engineering, model evaluation, and visualization to inform business decisions.
Use Case: Forecast demand, detect anomalies, and reveal temporal patterns in finance, retail, and operations.

Quick Start

Prepare a time-series dataset and run the time-series skill to generate forecasts for the next 30 periods.

Frequently Asked Questions about time-series

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

FAQPage Schema

How do I forecast time-series data with multiple models like ARIMA and Prophet?▼

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.

What is the best way to detect anomalies in temporal retail or finance data?▼

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.

Do I need to test stationarity before using SARIMA for seasonality analysis?▼

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.

Can I use exponential smoothing for demand forecasting in operations?▼

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.

How do I prepare a dataset for time-series forecasting and visualization?▼

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.