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"""
Casos de uso para Forecasting.
Implementan la l贸gica de aplicaci贸n para pron贸sticos,
orquestando servicios de dominio y transformando DTOs.
"""
from typing import List
from app.domain.services.forecast_service import ForecastService
from app.domain.models.time_series import TimeSeries
from app.domain.models.forecast_config import ForecastConfig
from app.application.dtos.forecast_dtos import (
ForecastInputDTO,
ForecastOutputDTO,
MultiForecastInputDTO,
MultiForecastOutputDTO,
SeriesInputDTO
)
from app.utils.logger import setup_logger
logger = setup_logger(__name__)
class ForecastUnivariateUseCase:
"""
Caso de uso: Pron贸stico Univariado.
Responsabilidad: Ejecutar pron贸stico para una serie temporal 煤nica.
"""
def __init__(self, forecast_service: ForecastService):
"""
Inicializa el caso de uso.
Args:
forecast_service: Servicio de dominio para forecasting
"""
self.forecast_service = forecast_service
logger.info("ForecastUnivariateUseCase initialized")
def execute(self, input_dto: ForecastInputDTO) -> ForecastOutputDTO:
"""
Ejecuta el caso de uso.
Args:
input_dto: Datos de entrada validados
Returns:
ForecastOutputDTO: Resultado del pron贸stico
Raises:
ValueError: Si los datos son inv谩lidos
RuntimeError: Si falla el pron贸stico
"""
logger.info(f"Executing forecast for series: {input_dto.series_id}")
# Validar entrada
input_dto.validate()
# Convertir DTO a modelos de dominio
series = TimeSeries(
values=input_dto.values,
timestamps=input_dto.timestamps,
series_id=input_dto.series_id,
freq=input_dto.freq
)
config = ForecastConfig(
prediction_length=input_dto.prediction_length,
quantile_levels=input_dto.quantile_levels,
freq=input_dto.freq
)
# Ejecutar servicio de dominio
try:
result = self.forecast_service.forecast_univariate(series, config)
logger.info(f"Forecast completed: {len(result.timestamps)} periods")
except Exception as e:
logger.error(f"Forecast failed: {e}", exc_info=True)
raise RuntimeError(f"Forecast execution failed: {str(e)}") from e
# Convertir resultado a DTO
output_dto = ForecastOutputDTO(
timestamps=result.timestamps,
median=result.median,
quantiles=result.quantiles,
series_id=result.series_id,
metadata={
"prediction_length": config.prediction_length,
"freq": config.freq,
"context_length": len(series.values)
}
)
return output_dto
class ForecastMultiSeriesUseCase:
"""
Caso de uso: Pron贸stico Multi-Series.
Responsabilidad: Ejecutar pron贸sticos para m煤ltiples series.
"""
def __init__(self, forecast_service: ForecastService):
"""
Inicializa el caso de uso.
Args:
forecast_service: Servicio de dominio para forecasting
"""
self.forecast_service = forecast_service
logger.info("ForecastMultiSeriesUseCase initialized")
def execute(self, input_dto: MultiForecastInputDTO) -> MultiForecastOutputDTO:
"""
Ejecuta el caso de uso para m煤ltiples series.
Args:
input_dto: Datos de entrada con m煤ltiples series
Returns:
MultiForecastOutputDTO: Resultados de todos los pron贸sticos
"""
logger.info(f"Executing forecast for {len(input_dto.series_list)} series")
# Validar entrada
input_dto.validate()
# Configuraci贸n compartida
config = ForecastConfig(
prediction_length=input_dto.prediction_length,
quantile_levels=input_dto.quantile_levels,
freq=input_dto.freq
)
# Convertir DTOs a modelos de dominio
time_series_list: List[TimeSeries] = []
for series_dto in input_dto.series_list:
series = TimeSeries(
values=series_dto.values,
timestamps=series_dto.timestamps,
series_id=series_dto.series_id,
freq=input_dto.freq
)
time_series_list.append(series)
# Ejecutar servicio de dominio
results = []
successful = 0
failed = 0
for ts in time_series_list:
try:
result = self.forecast_service.forecast_univariate(ts, config)
output_dto = ForecastOutputDTO(
timestamps=result.timestamps,
median=result.median,
quantiles=result.quantiles,
series_id=result.series_id,
metadata={
"prediction_length": config.prediction_length,
"freq": config.freq,
"context_length": len(ts.values)
}
)
results.append(output_dto)
successful += 1
except Exception as e:
logger.error(f"Forecast failed for series {ts.series_id}: {e}")
failed += 1
# Continuar con las siguientes series
logger.info(f"Multi-series forecast completed: {successful} successful, {failed} failed")
# Crear DTO de salida
multi_output = MultiForecastOutputDTO(
results=results,
total_series=len(input_dto.series_list),
successful=successful,
failed=failed
)
return multi_output
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