Can I use this for real-time business forecasting?

Yes, it is designed to handle business data like sales, inventory, and traffic to provide insights for decision-making.

Does it require manual data cleaning?

While clean data is recommended for best results, the skill analyzes data characteristics and provides guidance on the quality and structure needed for accurate forecasting.

How do I trigger the forecasting capability?

You can trigger the skill using natural language terms like 'forecast', 'predict future values', or 'analyze trends' followed by a reference to your temporal data.

What forecasting models does this skill support?

The skill supports a variety of models including ARIMA, Prophet, and Nixtla's TimeGPT, automatically selecting the best fit for your specific dataset.

Time Series Forecaster

Name: Time Series Forecaster
Author: intent-solutions-io

byintent-solutions-io

Data Science & ML

Automates time series analysis and forecasting to predict future trends and seasonal patterns using advanced machine learning models.

About

This skill empowers Claude to transform historical temporal data into actionable future insights by automating the end-to-end forecasting pipeline. It handles everything from identifying data characteristics like seasonality and trends to selecting the optimal model—whether it be traditional ARIMA, Prophet, or Nixtla's TimeGPT. Ideal for developers and data scientists, this skill enables the rapid generation of predictions, model benchmarking, and even the creation of FastAPI services to serve forecasts, all through natural language commands within the Claude Code environment.

Key Features

Model performance benchmarking and evaluation metrics
Support for generating FastAPI services for model deployment
Intelligent model selection including ARIMA, Prophet, and TimeGPT
Automated identification of trends, seasonality, and autocorrelation
Generation of future predictions with detailed confidence intervals
0 GitHub stars

Use Cases

Forecasting server load and website traffic for infrastructure planning
Predicting quarterly sales and revenue growth based on historical records
Analyzing financial market trends and stock price fluctuations

About

Key Features

Model performance benchmarking and evaluation metrics
Support for generating FastAPI services for model deployment
Intelligent model selection including ARIMA, Prophet, and TimeGPT
Automated identification of trends, seasonality, and autocorrelation
Generation of future predictions with detailed confidence intervals
0 GitHub stars

Use Cases

Forecasting server load and website traffic for infrastructure planning
Predicting quarterly sales and revenue growth based on historical records
Analyzing financial market trends and stock price fluctuations