Can I use this for business sales forecasting?

Yes, it is specifically designed to identify trends and seasonality in sales data to provide accurate quarterly or monthly projections.

Does it provide confidence intervals for its predictions?

Yes, the skill generates future value predictions along with confidence intervals to help you understand the statistical uncertainty of the forecast.

What kind of data does the Time Series Forecaster require?

It requires historical, time-dependent numerical data that is clean and consistently formatted to produce the most accurate results.

What forecasting models does this Claude skill use?

The skill intelligently selects the best model based on data characteristics, typically utilizing ARIMA for stationary data or Prophet for data with strong seasonal components.

Can this skill help with DevOps and infrastructure planning?

Absolutely. It can analyze past server metrics or traffic logs to forecast future resource requirements and prevent downtime.

Time Series Forecaster

Name: Time Series Forecaster
Author: BbgnsurfTech

byBbgnsurfTech

•

数据科学与机器学习

Forecasts future values and identifies temporal patterns by analyzing historical time-series data using advanced statistical models.

关于

The Time Series Forecaster skill empowers Claude to perform sophisticated data analysis and predictive modeling directly within your workflow. By analyzing historical time-dependent data, it identifies underlying trends, seasonality, and autocorrelation to select the most appropriate forecasting model, such as ARIMA or Prophet. This skill is ideal for developers and analysts who need to project future metrics, plan resources, or gain insights into temporal data patterns without manually configuring complex statistical environments.

主要功能

Comprehensive analysis of historical data characteristics
3 GitHub stars
Generation of future value predictions with confidence intervals
Identification of seasonal patterns and long-term trends
Automated model selection including ARIMA and Prophet
Support for diverse temporal frequencies like daily, weekly, or monthly data

使用场景

Forecasting website traffic and server load for capacity planning
Analyzing stock price trends or financial metrics over time
Predicting quarterly sales revenue based on multi-year historical data

关于

主要功能

Comprehensive analysis of historical data characteristics
3 GitHub stars
Generation of future value predictions with confidence intervals
Identification of seasonal patterns and long-term trends
Automated model selection including ARIMA and Prophet
Support for diverse temporal frequencies like daily, weekly, or monthly data

使用场景

Forecasting website traffic and server load for capacity planning
Analyzing stock price trends or financial metrics over time
Predicting quarterly sales revenue based on multi-year historical data