Benchmark Industrial Forecasting Models with TimesFM and Darts

"""
Production implementation for Benchmark Industrial Forecasting Models with TimesFM and Darts.
Provides secure, scalable operations.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import pandas as pd
import numpy as np
import time
import backoff
from darts import TimeSeries
from darts.models import ExponentialSmoothing
from darts.utils.timeseries_generation import datetime_attribute_timeseries

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

class Config:
    """
    Configuration class to manage environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///forecasting.db')
    timesfm_model_path: str = os.getenv('TIMESFM_MODEL_PATH', 'model/')

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 'time_series' not in data:
        raise ValueError('Missing time_series data')
    if not isinstance(data['time_series'], list):
        raise ValueError('time_series must be a list')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields for processing.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    sanitized = {key: str(value).strip() for key, value in data.items()}
    return sanitized

@backoff.on_exception(backoff.expo, Exception, max_tries=5)
def fetch_data() -> List[Dict[str, Any]]:
    """Fetch data from a database or API.
    
    Returns:
        List of records
    Raises:
        Exception: If data fetching fails
    """
    # Simulate fetching data
    logger.info('Fetching data...')
    time.sleep(1)  # Simulated delay
    return [{'time_series': [1, 2, 3, 4, 5]}]  # Example data

def transform_records(records: List[Dict[str, Any]]) -> TimeSeries:
    """Transform raw records into a TimeSeries object.
    
    Args:
        records: Raw input records
    Returns:
        TimeSeries object
    """
    series_data = [record['time_series'] for record in records]
    return TimeSeries.from_values(series_data)

def aggregate_metrics(series: TimeSeries) -> Dict[str, float]:
    """Aggregate metrics from the TimeSeries.
    
    Args:
        series: TimeSeries object
    Returns:
        Dictionary of aggregated metrics
    """
    mean = np.mean(series.values())
    std = np.std(series.values())
    return {'mean': mean, 'std': std}

class ForecastingModel:
    """Main orchestrator for forecasting models.
    """
    def __init__(self, series: TimeSeries):
        self.series = series
        self.model = ExponentialSmoothing()

    def fit(self) -> None:
        """Fit the forecasting model.
        """
        logger.info('Fitting model...')
        self.model.fit(self.series)

    def forecast(self, steps: int) -> TimeSeries:
        """Forecast future values.
        
        Args:
            steps: Number of steps to forecast
        Returns:
            TimeSeries of forecasted values
        """
        logger.info('Forecasting...')
        return self.model.predict(steps)

def save_to_db(data: Dict[str, Any]) -> None:
    """Save results to a database.
    
    Args:
        data: Data to save
    Raises:
        Exception: If saving fails
    """
    logger.info('Saving data to database...')
    # Simulate saving data
    time.sleep(1)  # Simulated delay

if __name__ == '__main__':
    # Example usage
    try:
        raw_data = fetch_data()  # Fetching data
        for record in raw_data:
            validated = validate_input(record)  # Validate input
            sanitized = sanitize_fields(record)  # Sanitize input
            series = transform_records([sanitized])  # Transform input
            model = ForecastingModel(series)  # Initialize model
            model.fit()  # Fit model
            forecast = model.forecast(steps=5)  # Forecasting
            metrics = aggregate_metrics(series)  # Aggregate metrics
            logger.info(f'Forecast: {forecast}, Metrics: {metrics}')  # Log results
            save_to_db({'forecast': forecast, 'metrics': metrics})  # Save results
    except Exception as e:
        logger.error(f'An error occurred: {e}')  # Log error