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Normann 2025-01-07 00:30:53 +01:00 committed by Andreas
parent e0bdbdcb81
commit 3121490a23
2 changed files with 17 additions and 17 deletions
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src/akkudoktoreos/prediction/elecpriceakkudoktor.py
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@ -12,13 +12,13 @@ import numpy as np
import requests import requests
from numpydantic import NDArray, Shape from numpydantic import NDArray, Shape
from pydantic import Field, ValidationError from pydantic import Field, ValidationError
from statsmodels.tsa.holtwinters import ExponentialSmoothing
from akkudoktoreos.core.logging import get_logger from akkudoktoreos.core.logging import get_logger
from akkudoktoreos.core.pydantic import PydanticBaseModel from akkudoktoreos.core.pydantic import PydanticBaseModel
from akkudoktoreos.prediction.elecpriceabc import ElecPriceDataRecord, ElecPriceProvider from akkudoktoreos.prediction.elecpriceabc import ElecPriceDataRecord, ElecPriceProvider
from akkudoktoreos.utils.cacheutil import cache_in_file from akkudoktoreos.utils.cacheutil import cache_in_file
from akkudoktoreos.utils.datetimeutil import to_datetime, to_duration from akkudoktoreos.utils.datetimeutil import to_datetime, to_duration
from statsmodels.tsa.holtwinters import ExponentialSmoothing
logger = get_logger(__name__) logger = get_logger(__name__)
@ -122,18 +122,18 @@ class ElecPriceAkkudoktor(ElecPriceProvider):
self.update_datetime = to_datetime(in_timezone=self.config.timezone) self.update_datetime = to_datetime(in_timezone=self.config.timezone)
return akkudoktor_data return akkudoktor_data
def cap_outliers(data: np.ndarray, sigma: int = 2) -> np.ndarray: def _cap_outliers(self, data: np.ndarray, sigma: int = 2) -> np.ndarray:
mean = data.mean() mean = data.mean()
std = data.std() std = data.std()
lower_bound = mean - sigma * std lower_bound = mean - sigma * std
upper_bound = mean + sigma * std upper_bound = mean + sigma * std
capped_data = data.clip(lower=lower_bound, upper=upper_bound) capped_data = data.clip(min=lower_bound, max=upper_bound)
return capped_data return capped_data
def predict_ets( def _predict_ets(
history: np.ndarray, seasonal_periods: int, prediction_hours: int self, history: np.ndarray, seasonal_periods: int, prediction_hours: int
) -> np.ndarray: ) -> np.ndarray:
clean_history = cap_outliers(history) clean_history = self._cap_outliers(history)
model = ExponentialSmoothing( model = ExponentialSmoothing(
clean_history, seasonal="add", seasonal_periods=seasonal_periods clean_history, seasonal="add", seasonal_periods=seasonal_periods
).fit() ).fit()