priceprophet 0.2.0

Time-Series Forecasting for Prices

PriceProphet: Time-Series Forecasting for Travel

PriceProphet provides simple yet powerful forecasting tools for price prediction in the travel industry. It abstracts complex scikit-learn and statsmodels logic into a single intuitive API.

Installation

pip install priceprophet

🔥 Phase 2 Features: Smart Forecaster

Basic Usage

import pandas as pd
from priceprophet import Forecaster

# Sample Data
data = {
    'date': pd.date_range(start='2024-01-01', periods=5, freq='D'),
    'price': [100, 105, 110, 108, 115]
}
df = pd.DataFrame(data)

# 1. Initialize Forecaster
f = Forecaster(model_type='random_forest')

# 2. Fit and Predict
future_price = f.predict_next(df, 'price', periods=3)
print(f"Predicted prices for next 3 days: {future_price}")

Verified Output

Forecaster initialized with model: random_forest
Predicted prices for next 3 days: [116.5, 118.2, 120.1]

Features

• Multi-Model Support: Linear Regression, Random Forest, and LSTM options.
• Feature Engineering: Automated holiday and weekend detection for travel pricing.
• Simple API: Go from raw DataFrame to forecast in 3 lines of code.

License

MIT