Forecast Logistics Demand Patterns with statsforecast and Prophet

                      
                     
"""
Production implementation for forecasting logistics demand patterns using statsforecast and Prophet.
Provides secure, scalable operations for demand prediction in logistics.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import pandas as pd
from statsforecast import StatsForecast
from statsforecast.models import Prophet
from sqlalchemy import create_engine, text
import time

# Configure logging to capture various levels of events
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to handle environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///:memory:')

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'logistics_data' not in data:
        raise ValueError('Missing logistics_data key')
    if not isinstance(data['logistics_data'], List):
        raise ValueError('logistics_data must be a list')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input data.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    # Example: remove any leading/trailing whitespace from keys
    return {k.strip(): v for k, v in data.items()}

def fetch_data(query: str) -> pd.DataFrame:
    """Fetch data from the database.
    
    Args:
        query: SQL query to execute
    Returns:
        DataFrame containing the results
    Raises:
        Exception: If query fails
    """
    try:
        engine = create_engine(Config.database_url)
        with engine.connect() as conn:
            df = pd.read_sql(query, conn)
        return df
    except Exception as e:
        logger.error(f'Error fetching data: {e}')
        raise

def save_to_db(df: pd.DataFrame, table_name: str) -> None:
    """Save DataFrame to the database.
    
    Args:
        df: DataFrame to save
        table_name: Target table name
    Raises:
        Exception: If save fails
    """
    try:
        engine = create_engine(Config.database_url)
        with engine.connect() as conn:
            df.to_sql(table_name, conn, if_exists='replace', index=False)
        logger.info(f'Successfully saved data to {table_name}')
    except Exception as e:
        logger.error(f'Error saving to database: {e}')
        raise

def transform_records(data: List[Dict[str, Any]]) -> pd.DataFrame:
    """Transform raw records into a DataFrame suitable for forecasting.
    
    Args:
        data: List of records to transform
    Returns:
        DataFrame ready for statsforecast
    """
    # Convert raw data to DataFrame
    df = pd.DataFrame(data)
    df['date'] = pd.to_datetime(df['date'])  # Ensure date is in correct format
    return df

def process_batch(df: pd.DataFrame) -> pd.DataFrame:
    """Process and forecast logistics demand.
    
    Args:
        df: DataFrame containing logistics data
    Returns:
        DataFrame with forecasts
    """
    model = StatsForecast(df, models=[Prophet()])
    forecasts = model.forecast()
    return forecasts

class ForecastingOrchestrator:
    """Main orchestrator for the forecasting process.
    """
    def __init__(self, data: Dict[str, Any]):
        self.data = sanitize_fields(data)
        validate_input(self.data)

    def run_forecasting(self) -> None:
        """Run the forecasting process.
        """
        try:
            # Fetch data
            df = fetch_data('SELECT * FROM logistics_data')
            # Transform data
            transformed_df = transform_records(self.data['logistics_data'])
            # Process forecasts
            forecasts = process_batch(transformed_df)
            # Save results
            save_to_db(forecasts, 'forecast_results')
        except ValueError as ve:
            logger.warning(f'ValueError: {ve}')
        except Exception as e:
            logger.error(f'An error occurred: {e}')

if __name__ == '__main__':
    # Example usage
    input_data = {
        'logistics_data': [
            {'date': '2023-01-01', 'demand': 100},
            {'date': '2023-01-02', 'demand': 150},
        ]
    }
    orchestrator = ForecastingOrchestrator(input_data)
    orchestrator.run_forecasting()
    logger.info('Forecasting process completed.');