Forecasting for Revenue Planning

import pandas as pd
from prophet import Prophet
import numpy as np
from typing import Dict, List

class HierarchicalForecaster:
    def __init__(self):
        self.models = {}
        self.hierarchy_levels = ['total', 'region', 'product', 'channel']

    def fit_hierarchical_models(self, data: pd.DataFrame):
        """Fit Prophet models at each hierarchy level"""
        for level in self.hierarchy_levels:
            level_data = self.aggregate_by_level(data, level)

            for series_id in level_data['series_id'].unique():
                series_data = level_data[level_data['series_id'] == series_id]

                # Configure Prophet with seasonality
                model = Prophet(
                    yearly_seasonality=True,
                    weekly_seasonality=False,
                    daily_seasonality=False,
                    interval_width=0.95,
                    changepoint_prior_scale=0.05
                )

                # Add custom regressors
                model.add_regressor('marketing_spend')
                model.add_regressor('economic_index')

                # Prepare data for Prophet
                prophet_data = series_data[['ds', 'y', 'marketing_spend', 'economic_index']]
                model.fit(prophet_data)

                self.models[f"{level}_{series_id}"] = model

    def generate_forecasts(self, periods: int = 90) -> Dict[str, pd.DataFrame]:
        """Generate probabilistic forecasts with reconciliation"""
        forecasts = {}

        for model_key, model in self.models.items():
            future = model.make_future_dataframe(periods=periods)
            future = self.add_future_regressors(future, model_key)

            forecast = model.predict(future)
            forecasts[model_key] = forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]

        # Reconcile hierarchical forecasts
        return self.reconcile_forecasts(forecasts)