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price-prediction

Forecast price data with ETS, ARIMA, SARIMAX, or Prophet models.

Updated Feb 24, 2026
Search Tags:#forecasting#time-series#data-analysis#arima#prophet#price-prediction
One-click install
npx skills add https://github.com/amitpo23/medici-price-prediction --skill price-prediction
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: price-prediction
Source: https://github.com/amitpo23/medici-price-prediction/tree/main/skills/price-prediction
Command: npx skills add https://github.com/amitpo23/medici-price-prediction --skill price-prediction

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Predicts price movements from historical data to inform pricing decisions, budgeting, and strategic planning.

Core Features & Use Cases

  • Multi-model forecasting using ETS, ARIMA/SARIMAX, and Prophet to capture trends, seasonality, and autocorrelation.
  • Generates a clear directional price signal with confidence intervals, enabling quick decisions.
  • Guidance on model selection based on data size and seasonality, plus visualization of forecasts and uncertainty.

Quick Start

Provide a forecast for your price data using the models described.

Frequently Asked Questions about price-prediction

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I forecast price data using time-series models?▼

For time-series price forecasting, ETS suits small datasets, ARIMA handles 30-100 points, SARIMAX captures seasonality, and Prophet fits 100+ daily points with holidays. The skill automatically selects based on your data characteristics.

When should I use Prophet vs ARIMA for price prediction?▼

Prophet is recommended for 100+ data points with daily seasonality and holiday effects, while ARIMA suits 30-100 point datasets. SARIMAX is chosen when explicit seasonal patterns need modeling in the price data.

Do I need to install statsmodels and prophet to run price forecasting?▼

Core libraries like statsmodels, pandas, numpy, scipy, and plotly are required for price forecasting. Optional dependencies such as pmdarima and prophet have built-in fallbacks when unavailable.

Can I forecast prices with a small dataset?▼

Small datasets use the ETS model for price forecasting. If data grows beyond 30 points, the system transitions to ARIMA, SARIMAX for seasonality, or Prophet for larger daily datasets.

How does the forecasting model handle confidence intervals?▼

Price forecasting models generate a directional signal alongside confidence intervals to quantify uncertainty. This enables quick pricing decisions by visualizing forecast ranges and potential price movement.

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