How do I trigger a forecast with Claude?

You can trigger the skill using natural language requests containing terms like 'forecast', 'predict', 'time series analysis', or 'future values' followed by your data source.

Does it provide a range of possible outcomes?

Yes, the skill generates predictions along with confidence intervals, providing a statistical range of probable future values rather than just a single data point.

What forecasting models does this skill use?

The skill intelligently selects the most appropriate model based on your data's characteristics, typically utilizing ARIMA for stationary data or Prophet for data with strong seasonal patterns.

Can it handle missing data points in a time series?

For optimal results, data should be clean and complete; however, the analysis phase can often identify gaps and suggest preprocessing steps before generating a forecast.

Time Series Forecasting

Name: Time Series Forecasting
Author: jeremylongshore

byjeremylongshore

•

884

Data Science & ML

Predicts future values and identifies temporal patterns in historical data using advanced statistical modeling and automated analysis.

About

The Time Series Forecasting skill equips Claude with the ability to analyze historical time-dependent data to project future trends, seasonality, and cycles. By automating the complex processes of data analysis and model selection—including frameworks like ARIMA and Prophet—it enables developers and analysts to generate accurate predictions for sales, web traffic, or financial metrics directly within their terminal. This skill is essential for data-driven decision-making, providing not just raw numbers but also confidence intervals and insights into temporal patterns.

Key Features

Intelligent selection of forecasting models like ARIMA and Prophet
Integration-ready outputs for data visualization and reporting
Generation of future value predictions with confidence intervals
884 GitHub stars
Comprehensive analysis of historical temporal data patterns
Automated trend, seasonality, and autocorrelation detection

Use Cases

Predicting server load or website traffic for infrastructure capacity planning
Analyzing and projecting stock price trends or financial market indicators
Forecasting quarterly sales revenue based on multi-year monthly data

About

Key Features

Intelligent selection of forecasting models like ARIMA and Prophet
Integration-ready outputs for data visualization and reporting
Generation of future value predictions with confidence intervals
884 GitHub stars
Comprehensive analysis of historical temporal data patterns
Automated trend, seasonality, and autocorrelation detection

Use Cases

Predicting server load or website traffic for infrastructure capacity planning
Analyzing and projecting stock price trends or financial market indicators
Forecasting quarterly sales revenue based on multi-year monthly data