Metadata-Version: 2.4
Name: priceprophet
Version: 0.3.0
Summary: Time-Series Forecasting for Prices
Author-email: Teyfik OZ <teyfikoz@yahoo.com>
License: MIT
Project-URL: Homepage, https://github.com/teyfikoz/priceprophet
Project-URL: Repository, https://github.com/teyfikoz/priceprophet
Project-URL: Issues, https://github.com/teyfikoz/priceprophet/issues
Keywords: forecasting,time-series,pricing,anomaly-detection,market-analysis
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Requires-Python: >=3.10
Description-Content-Type: text/markdown
Requires-Dist: numpy>=1.24.0
Requires-Dist: pandas>=2.0.0
Requires-Dist: scikit-learn>=1.3.0
Provides-Extra: dev
Requires-Dist: pytest>=7.0; extra == "dev"
Requires-Dist: ruff>=0.1.0; extra == "dev"

# PriceProphet

Time-Series Forecasting for Prices with anomaly detection and impact simulation.

[![PyPI version](https://badge.fury.io/py/priceprophet.svg)](https://pypi.org/project/priceprophet/)
[![CI](https://github.com/teyfikoz/priceprophet/actions/workflows/ci.yml/badge.svg)](https://github.com/teyfikoz/priceprophet/actions/workflows/ci.yml)

## Installation

```bash
pip install priceprophet
```

## Quick Start

```python
from priceprophet import PriceProphet
import pandas as pd

pp = PriceProphet()

# Create sample data
df = pd.DataFrame({
    'date': pd.date_range('2025-01-01', periods=90),
    'price': [100 + i * 0.5 + (i % 7) * 2 for i in range(90)]
})

# Forecast future prices
forecast = pp.forecast(df, periods=30)
print(forecast.head())

# Detect anomalies
anomalies = pp.detect_anomalies(df)
print(anomalies[anomalies['is_anomaly']])

# Simulate market impact
impact = pp.simulate_impact(current_price=150.0, shock_magnitude=0.15, shock_type="competitor")
print(impact)
```

## Features

- **Price Forecasting** - Linear regression with dynamic confidence intervals
- **Anomaly Detection** - Z-score based anomaly identification
- **Impact Simulation** - Market shock simulation (competitor, supply, demand, regulation)

## License

MIT
