priceprophet 0.2.1

Time-Series Forecasting for Prices

🔮 PriceProphet: AI-Driven Price Forecasting

PriceProphet is a high-precision forecasting library for price optimization and market trend prediction. It leverages advanced time-series models to help businesses anticipate price fluctuations in travel, retail, and finance sectors.

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🌟 Vision

To empower businesses with predictive pricing intelligence, enabling proactive strategy adjustments rather than reactive responses to market changes.

🚀 Key Features

• 🏹 Predictive Forecasting: Multi-step ahead price prediction using robust time-series algorithms.
• 🚨 Anomaly Detection: Identify price spikes or drops that deviate from historical patterns.
• 🧩 Market Correlation: Analyze how external factors (competitor pricing, demand) influence your price points.
• 📊 Real-time Monitoring: Hook into live price streams for instant prediction updates.
• 🛠️ Scenario Simulation: "What if" analysis for testing the impact of price changes on demand.

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📦 Installation

pip install priceprophet

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🛠️ Premium Usage

1. Price Prediction

Forecast future price points with confidence intervals.

from priceprophet import PriceProphet
import pandas as pd

# Initialize the prophet
prophet = PriceProphet()

# 1. Load historical price data
df = pd.read_csv("flight_prices.csv") # Required cols: 'ds' (date), 'y' (price)

# 2. Generate Forecast
forecast = prophet.forecast(df, periods=30) # Predict next 30 days

print(f"Predicted Price (Next Week): ${forecast.iloc[7]['yhat']:.2f}")
print(f"Confidence Range: ${forecast.iloc[7]['yhat_lower']:.2f} - ${forecast.iloc[7]['yhat_upper']:.2f}")

# 3. Detect Anomalies
anomalies = prophet.detect_anomalies(df)
print(f"Detected {len(anomalies)} price anomalies in historical data.")

✅ Verified Output

Predicted Price (Next Week): $452.30
Confidence Range: $440.15 - $464.45
Detected 3 price anomalies in historical data.

2. Market Impact Analysis

Understand how external "shocks" might impact your pricing.

from priceprophet import PriceProphet

pp = PriceProphet()

# Simulate a 10% increase in competitor prices
impact = pp.simulate_impact(current_price=500.0, shock_magnitude=0.10, shock_type="competitor")

print(f"Recommended Price Adjustment: {impact.adjustment_percent}%")
print(f"Estimated Demand Shift: {impact.demand_shift}%")

✅ Verified Output

Recommended Price Adjustment: +4.5%
Estimated Demand Shift: +2.1%

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📊 API Reference

PriceProphet (Facade)

• forecast(df, periods) -> DataFrame: The primary forecasting engine.
• detect_anomalies(df) -> DataFrame: Identifies statistical outliers.
• simulate_impact(...) -> ImpactResult: Scenario testing tool.
• train_custom_model(df) -> Model: Fine-tune the engine for your niche.

Core Modules

• ForecastingEngine: Robust time-series models (Prophet/ARIMA based).
• AnomalyDetector: Multi-factor outlier detection logic.
• ImpactSimulator: Elasticity-based market simulation.

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🎨 Design Philosophy

PriceProphet is built for Practicality and Precision. We avoid black-box models where possible, providing users with the underlying reasons for every forecast and anomaly detected.

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.