What triggers the Time Series Forecaster skill?

It is triggered by natural language requests involving terms like 'forecast', 'predict', 'time series analysis', or requests regarding future values of temporal data.

Can it handle missing data points in my time series?

While the skill performs analysis, it is a best practice to provide clean and complete datasets to ensure the highest accuracy in predictions and trend identification.

Does it provide uncertainty measurements?

Yes, when generating forecasts, the skill includes confidence intervals to provide a range of probable future values rather than just a single point estimate.

What forecasting models does this skill use?

The skill intelligently selects the most appropriate model based on data characteristics, typically utilizing ARIMA for stationary data or Prophet for data with strong seasonality.

Time Series Forecaster

Name: Time Series Forecaster
Author: BbgnsurfTech

byBbgnsurfTech

•

データサイエンスとML

Analyzes historical time-dependent data to predict future values and identify underlying trends or seasonal patterns.

概要

This skill empowers Claude to perform sophisticated time series forecasting by automating data analysis, model selection, and prediction generation. By identifying patterns such as seasonality and autocorrelation, it can apply appropriate models like ARIMA or Prophet to provide actionable insights for sales, website traffic, or market trends, complete with confidence intervals for informed decision-making within the Claude Code environment.

主な機能

Automated trend and seasonality identification
Support for diverse temporal data analysis
Intelligent model selection including ARIMA and Prophet
Historical data pattern and autocorrelation analysis
Future value prediction with confidence intervals
3 GitHub stars

ユースケース

Predicting future website traffic patterns for infrastructure scaling
Analyzing stock price trends and market volatility over time
Forecasting quarterly sales based on multi-year historical records

概要

主な機能

Automated trend and seasonality identification
Support for diverse temporal data analysis
Intelligent model selection including ARIMA and Prophet
Historical data pattern and autocorrelation analysis
Future value prediction with confidence intervals
3 GitHub stars

ユースケース

Predicting future website traffic patterns for infrastructure scaling
Analyzing stock price trends and market volatility over time
Forecasting quarterly sales based on multi-year historical records