| from typing import List, Optional | |
| from gluonts.dataset.common import Dataset | |
| from gluonts.model.forecast import Forecast | |
| class AbstractPredictor: | |
| def __init__( | |
| self, | |
| prediction_length: int, | |
| freq: str, | |
| seasonality: int, | |
| quantile_levels: Optional[List[float]] = None, | |
| ): | |
| self.prediction_length = prediction_length | |
| self.freq = freq | |
| self.seasonality = seasonality | |
| self.quantile_levels = quantile_levels or [ | |
| 0.1, | |
| 0.2, | |
| 0.3, | |
| 0.4, | |
| 0.5, | |
| 0.6, | |
| 0.7, | |
| 0.8, | |
| 0.9, | |
| ] | |
| self._runtime = None | |
| def fit_predict( | |
| self, | |
| dataset: Dataset | |
| ) -> List[Forecast]: | |
| raise NotImplementedError | |
| def save_runtime(self, time: float) -> None: | |
| self._runtime = time | |
| def get_runtime(self) -> float: | |
| return self._runtime | |