Build Real-Time Production Forecasts with TimeGPT-1 and Darts

                      
                     
"""
Production implementation for building real-time production forecasts using TimeGPT-1 and Darts.
Provides a scalable, efficient pipeline for data processing and forecasting.
"""

from typing import Dict, Any, List
import os
import logging
import time
import requests
from darts import TimeSeries
from darts.models import Prophet
from darts.utils.statistics import train_test_split
from contextlib import contextmanager

# Logger setup for tracking application events
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class for environment variables
    """
    darts_api_key: str = os.getenv('DARTS_API_KEY')
    database_url: str = os.getenv('DATABASE_URL')
    retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', 3))

@contextmanager
def db_connection() -> Any:
    """Context manager for database connection.

    Yields:
        Connection object for database operations
    """
    conn = None
    try:
        # Initialize the database connection
        conn = connect_to_database(Config.database_url)
        yield conn  # Yield connection for use
    except Exception as e:
        logger.error(f"Database connection failed: {e}")
    finally:
        if conn:
            conn.close()  # Ensure the connection is closed

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for forecasting.
    
    Args:
        data: Dictionary containing input data
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'time_series' not in data:
        raise ValueError('Missing time_series key in input data')
    if not isinstance(data['time_series'], list):
        raise ValueError('time_series must be a list')
    return True

def fetch_data(api_url: str) -> List[Dict[str, Any]]:
    """Fetch data from the specified API.
    
    Args:
        api_url: API endpoint to fetch data
    Returns:
        List of records fetched
    Raises:
        ConnectionError: If the API call fails
    """
    try:
        response = requests.get(api_url)
        response.raise_for_status()  # Raise an error for bad responses
        return response.json()
    except requests.RequestException as e:
        logger.error(f"Failed to fetch data: {e}")
        raise ConnectionError(f"Failed to fetch data from {api_url}")

def normalize_data(raw_data: List[Dict[str, Any]]) -> TimeSeries:
    """Normalize raw data into a TimeSeries object.
    
    Args:
        raw_data: Raw data to normalize
    Returns:
        Normalized TimeSeries object
    """
    series = TimeSeries.from_dataframe(pd.DataFrame(raw_data))  # Convert to TimeSeries
    return series

def transform_records(series: TimeSeries) -> TimeSeries:
    """Transform the TimeSeries records as needed for forecasting.
    
    Args:
        series: TimeSeries object to transform
    Returns:
        Transformed TimeSeries object
    """
    return series  # Placeholder for transformation logic

def save_to_db(data: Any) -> None:
    """Save forecasted data to the database.
    
    Args:
        data: Data to save
    """
    with db_connection() as conn:
        # Save implementation here
        pass

async def process_batch(data: Dict[str, Any]) -> None:
    """Process a batch of input data for forecasting.
    
    Args:
        data: Input data for processing
    """
    validated = await validate_input(data)  # Validate input
    if validated:
        raw_data = fetch_data(data['api_url'])  # Fetch data
        time_series = normalize_data(raw_data)  # Normalize data
        transformed_series = transform_records(time_series)  # Transform data
        model = Prophet()  # Initialize forecasting model
        model.fit(transformed_series)  # Fit the model
        forecast = model.predict(n=10)  # Predict next 10 time points
        save_to_db(forecast)  # Save forecasted data

if __name__ == '__main__':
    # Example usage
    data = {'api_url': 'http://example.com/api/data', 'time_series': []}
    try:
        process_batch(data)
    except Exception as e:
        logger.error(f"Error processing batch: {e}")