Build Multi-Step Ahead Forecasts with PyTorch Forecasting and statsmodels

                      
                     
"""
Production implementation for building multi-step ahead forecasts.
Integrates PyTorch Forecasting and statsmodels for robust time series analysis.
"""

import os
import logging
import pandas as pd
from typing import Dict, Any, List, Tuple
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from statsmodels.tsa.holtwinters import ExponentialSmoothing
from pytorch_forecasting import TimeSeriesDataSet, TemporalFusionTransformer
from pytorch_forecasting import Trainer, AbsoluteLoss

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

# Configuration class for environment variables
class Config:
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///forecasts.db')
    model_path: str = os.getenv('MODEL_PATH', 'model.pth')

# Database session setup
engine = create_engine(Config.database_url)
session = sessionmaker(bind=engine)()

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

def fetch_data(query: str) -> pd.DataFrame:
    """Fetch data from the database.
    
    Args:
        query: SQL query to execute
    Returns:
        pd.DataFrame: Fetched data
    Raises:
        Exception: If database connection fails
    """
    try:
        logger.info('Fetching data from database')
        return pd.read_sql(query, session.bind)
    except Exception as e:
        logger.error(f'Error fetching data: {e}')
        raise

def preprocess_data(df: pd.DataFrame) -> pd.DataFrame:
    """Preprocess the input DataFrame.
    
    Args:
        df: Raw DataFrame
    Returns:
        pd.DataFrame: Processed DataFrame
    """
    df['date'] = pd.to_datetime(df['date'])  # Ensure date is in datetime format
    df.set_index('date', inplace=True)
    logger.info('Preprocessing data')
    return df

def create_datasets(df: pd.DataFrame, target_col: str) -> Tuple[TimeSeriesDataSet, TimeSeriesDataSet]:
    """Create training and validation datasets for PyTorch Forecasting.
    
    Args:
        df: DataFrame containing the time series data
        target_col: Column name of the target variable
    Returns:
        Tuple[TimeSeriesDataSet, TimeSeriesDataSet]: Training and validation datasets
    """
    logger.info('Creating datasets for forecasting')
    dataset = TimeSeriesDataSet(
        df,
        time_idx='date',
        target=target_col,
        group_ids=['series_id'],
        min_encoder_length=12,
        max_encoder_length=24,
        min_prediction_length=6,
        max_prediction_length=12,
    )
    train_data = dataset
    val_data = dataset
    return train_data, val_data

def train_model(train_data: TimeSeriesDataSet) -> TemporalFusionTransformer:
    """Train the forecasting model.
    
    Args:
        train_data: Training dataset
    Returns:
        TemporalFusionTransformer: Trained model
    """
    logger.info('Training the model')
    model = TemporalFusionTransformer.from_dataset(train_data)
    trainer = Trainer(max_epochs=5)
    trainer.fit(model, train_data)
    return model

def save_model(model: TemporalFusionTransformer, path: str) -> None:
    """Save the trained model to disk.
    
    Args:
        model: Trained model
        path: Path to save the model
    """
    logger.info(f'Saving model to {path}')
    torch.save(model.state_dict(), path)

def load_model(path: str) -> TemporalFusionTransformer:
    """Load the model from disk.
    
    Args:
        path: Path to the model
    Returns:
        TemporalFusionTransformer: Loaded model
    """
    logger.info(f'Loading model from {path}')
    model = TemporalFusionTransformer.load_from_checkpoint(path)
    return model

def forecast(model: TemporalFusionTransformer, data: TimeSeriesDataSet) -> List[float]:
    """Generate forecasts from the model.
    
    Args:
        model: Trained model
        data: Input dataset for forecasting
    Returns:
        List[float]: Forecasted values
    """
    logger.info('Generating forecasts')
    predictions = model.predict(data)
    return predictions

if __name__ == '__main__':
    try:
        # Example flow
        query = 'SELECT * FROM time_series_data'
        raw_data = fetch_data(query)
        processed_data = preprocess_data(raw_data)
        train_data, val_data = create_datasets(processed_data, target_col='value')
        model = train_model(train_data)
        save_model(model, Config.model_path)
        loaded_model = load_model(Config.model_path)
        forecasts = forecast(loaded_model, val_data)
        logger.info(f'Forecasts: {forecasts}')
    except Exception as e:
        logger.error(f'An error occurred: {e}')