What models does this skill use for forecasting?

The skill analyzes your data characteristics to select the most appropriate model, typically choosing between ARIMA for stationary data or Prophet for data with strong seasonality.

What kind of data format does the skill require?

The skill works best with clean, chronological datasets such as CSV files or lists of timestamped values representing historical records.

Can it handle seasonal data like holiday sales?

Yes, the skill is specifically designed to identify and account for seasonality, cycles, and recurring patterns in temporal datasets.

Do I need to be a data scientist to use this Claude Code skill?

No, the skill automates the complex parts of model selection and training, making professional-grade forecasting accessible through natural language requests.

Does it provide confidence intervals for its predictions?

Yes, the skill generates future values along with confidence intervals to help you understand the statistical reliability of the 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 models like ARIMA and Prophet.

About

This skill equips Claude with the ability to analyze time-dependent datasets to uncover underlying trends, seasonality, and cycles. By automating data analysis and model selection, it allows developers and analysts to generate accurate forecasts for metrics such as sales, website traffic, or financial trends. It is particularly useful for business intelligence and strategic planning tasks where understanding future trajectories based on historical performance is critical for informed decision-making.

Key Features

Generation of future predictions with confidence intervals
884 GitHub stars
Support for diverse data types including sales, traffic, and financial metrics
Automated identification of data trends and seasonality
Analysis of autocorrelation and temporal patterns
Intelligent selection of forecasting models like ARIMA and Prophet

Use Cases

Forecasting server load or website traffic to optimize resource allocation
Predicting quarterly sales revenue based on multi-year historical data
Analyzing stock price movements to identify long-term temporal trends

About

Key Features

Generation of future predictions with confidence intervals
884 GitHub stars
Support for diverse data types including sales, traffic, and financial metrics
Automated identification of data trends and seasonality
Analysis of autocorrelation and temporal patterns
Intelligent selection of forecasting models like ARIMA and Prophet

Use Cases

Forecasting server load or website traffic to optimize resource allocation
Predicting quarterly sales revenue based on multi-year historical data
Analyzing stock price movements to identify long-term temporal trends