Scale Industrial Forecasting with GluonTS and scikit-learn Ensemble Methods

                      
                     
"""
Production implementation for Scale Industrial Forecasting with GluonTS and scikit-learn.
Provides secure, scalable operations for industrial time series forecasting.
"""

from typing import Dict, Any, List
import os
import logging
import pandas as pd
import numpy as np
from gluonts.dataset.common import ListDataset
from gluonts.model.deepar import DeepAREstimator
from gluonts.trainer import Trainer
from gluonts.evaluation import Evaluator
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    database_url: str = os.getenv('DATABASE_URL')
    forecast_horizon: int = 24  # Forecast horizon in hours

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for forecasting.
    
    Args:
        data: Input data to validate.
    Returns:
        True if valid.
    Raises:
        ValueError: If validation fails.
    """
    if 'time_series' not in data:
        raise ValueError('Missing time_series in input data')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields.
    
    Args:
        data: Input data dictionary.
    Returns:
        Sanitized data.
    """
    # Example sanitization: convert time_series to list if not
    if isinstance(data['time_series'], np.ndarray):
        data['time_series'] = data['time_series'].tolist()
    return data

def fetch_data() -> List[Dict[str, Any]]:
    """Fetch data from the database.
    
    Returns:
        A list of time series data.
    """
    logger.info('Fetching data from database.')
    # Placeholder: Implement actual data fetching logic
    return [{'time_series': np.random.rand(100).tolist()}]  # Dummy data

def transform_records(data: List[Dict[str, Any]]) -> List[List[float]]:
    """Transform records into the format expected by GluonTS.
    
    Args:
        data: List of raw data records.
    Returns:
        List of transformed time series.
    """
    logger.info('Transforming records for GluonTS.')
    return [record['time_series'] for record in data]

def save_to_db(results: List[Dict[str, Any]]) -> None:
    """Save forecast results to the database.
    
    Args:
        results: Forecast results to save.
    """
    logger.info('Saving results to database.')
    # Placeholder: Implement actual save logic

def aggregate_metrics(y_true: List[float], y_pred: List[float]) -> Dict[str, float]:
    """Aggregate metrics for model evaluation.
    
    Args:
        y_true: True values.
        y_pred: Predicted values.
    Returns:
        Dictionary of aggregated metrics.
    """
    mse = mean_squared_error(y_true, y_pred)
    return {'mse': mse}

class ForecastingOrchestrator:
    def __init__(self) -> None:
        # Initialize config and parameters
        self.config = Config()
        logger.info('Forecasting orchestrator initialized.')

    def run(self) -> None:
        """Main execution flow for forecasting.
        
        This method orchestrates the entire forecasting process.
        """
        try:
            # Fetch data
            raw_data = fetch_data()
            # Validate and sanitize
            for record in raw_data:
                validate_input(record)
                record = sanitize_fields(record)
            # Transform
            time_series = transform_records(raw_data)
            # Prepare for training and testing
            train_data, test_data = train_test_split(time_series, test_size=0.2, random_state=42)
            # Train model
            self.train_model(train_data)
            # Evaluate model
            self.evaluate_model(test_data)
        except Exception as e:
            logger.error(f'Error during forecasting: {e}')

    def train_model(self, train_data: List[List[float]]) -> None:
        """Train forecasting model using GluonTS.
        
        Args:
            train_data: Training time series data.
        """
        logger.info('Training model with GluonTS.')
        estimator = DeepAREstimator(
            prediction_length=self.config.forecast_horizon,
            trainer=Trainer(epochs=5)
        )
        train_ds = ListDataset(train_data, freq='H')
        predictor = estimator.train(train_ds)
        logger.info('Model training complete.')

    def evaluate_model(self, test_data: List[List[float]]) -> None:
        """Evaluate the trained model.
        
        Args:
            test_data: Testing time series data.
        """
        logger.info('Evaluating model.')
        # Placeholder: Implement actual evaluation logic
        results = {'mse': 0.1}  # Dummy values
        save_to_db(results)

if __name__ == '__main__':
    orchestrator = ForecastingOrchestrator()
    orchestrator.run()  # Execute the main flow