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e317359 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 | from __future__ import annotations
from dataclasses import dataclass
from typing import Iterator, List, Optional
import numpy as np
from gluonts.dataset import Dataset as GluonDataset
from gluonts.model import Forecast
from gluonts.model.forecast import QuantileForecast
from gluonts.model.predictor import RepresentablePredictor
@dataclass
class _ForecastConfig:
quantile_levels: Optional[List[float]]
forecast_keys: List[str]
intervals: Optional[List[int]]
@classmethod
def from_quantiles(cls, quantile_levels: Optional[List[float]]):
forecast_keys = ["mean"]
intervals = None
if not quantile_levels:
return cls(quantile_levels=None, forecast_keys=forecast_keys, intervals=intervals)
intervals = sorted(
{
round(200 * (max(q, 1 - q) - 0.5))
for q in quantile_levels
}
)
forecast_keys = ["mean"] + [str(q) for q in quantile_levels]
return cls(
quantile_levels=quantile_levels,
forecast_keys=forecast_keys,
intervals=intervals,
)
class SeasonalNaivePredictor(RepresentablePredictor):
"""Lightweight seasonal naive baseline for pipeline smoke tests."""
def __init__(
self,
prediction_length: int,
season_length: int = 1,
quantile_levels: Optional[List[float]] = None,
):
super().__init__(prediction_length=prediction_length)
self.season_length = max(1, season_length)
self.config = _ForecastConfig.from_quantiles(quantile_levels)
def predict(self, dataset: GluonDataset, **kwargs) -> Iterator[Forecast]:
for entry in dataset:
target = np.asarray(entry["target"], dtype=np.float64)
univariate = target.ndim == 1
if univariate:
working = target.reshape(1, -1)
else:
working = target
history = working[:, -self.season_length :]
if history.shape[-1] == 0:
raise ValueError("Seasonal-Naive requires at least one observation")
tiled = np.tile(
history, (1, int(np.ceil(self.prediction_length / history.shape[-1])))
)
mean = tiled[:, : self.prediction_length]
if univariate:
mean = mean[0]
arrays = [mean]
if self.config.quantile_levels:
for q in self.config.quantile_levels:
arrays.append(mean + np.abs(mean) * (q - 0.5) * 0.05)
forecast_arrays = np.stack(arrays, axis=0)
yield QuantileForecast(
forecast_arrays=forecast_arrays,
forecast_keys=self.config.forecast_keys,
start_date=entry["start"] + target.shape[-1],
item_id=entry["item_id"],
)
class RepeatedPredictionQuantileMixin:
"""Interface for deriving quantiles from repeated deterministic predictions.
Models that do not natively produce quantiles can implement
``point_forecast_1d``. The default ``sample_forecast_1d`` repeatedly
perturbs that point forecast with bootstrapped in-sample residuals, then
converts the resulting sample paths into quantile forecasts.
"""
quantile_levels: Optional[List[float]]
num_samples: int
random_seed: int
def point_forecast_1d(self, history: np.ndarray) -> np.ndarray:
raise NotImplementedError
def residuals_1d(self, history: np.ndarray, fitted: np.ndarray | None = None) -> np.ndarray:
if fitted is not None and fitted.size == history.size:
residuals = history - fitted
else:
residuals = np.diff(history)
residuals = residuals[np.isfinite(residuals)]
if residuals.size == 0:
scale = np.nanstd(history)
if not np.isfinite(scale) or scale == 0:
scale = 1e-6
return np.array([scale], dtype=np.float64)
return residuals.astype(np.float64)
def sample_forecast_1d(self, history: np.ndarray) -> np.ndarray:
mean = self.point_forecast_1d(history).astype(np.float64)
residuals = self.residuals_1d(history)
rng = np.random.default_rng(self.random_seed + int(abs(np.nansum(history))) % 100000)
centered = residuals - float(np.nanmean(residuals))
if centered.size == 0 or np.nanstd(centered) == 0:
scale = float(np.nanstd(history))
centered = rng.normal(0.0, scale if scale > 0 else 1e-6, size=max(8, self.num_samples))
innovations = rng.choice(centered, size=(self.num_samples, self.prediction_length), replace=True)
return mean[None, :] + innovations
def quantile_forecast_arrays(self, history: np.ndarray) -> np.ndarray:
samples = self.sample_forecast_1d(history)
mean = np.mean(samples, axis=0)
arrays = [mean]
if self.quantile_levels:
arrays.extend(np.quantile(samples, q, axis=0) for q in self.quantile_levels)
return np.stack(arrays, axis=0).astype(np.float64)
class StatisticalBaselinePredictor(RepeatedPredictionQuantileMixin, RepresentablePredictor):
"""Base class for lightweight statistical baselines with quantile fallback."""
def __init__(
self,
prediction_length: int,
quantile_levels: Optional[List[float]] = None,
num_samples: int = 200,
random_seed: int = 0,
):
super().__init__(prediction_length=prediction_length)
self.quantile_levels = quantile_levels
self.num_samples = max(20, int(num_samples))
self.random_seed = int(random_seed)
self.config = _ForecastConfig.from_quantiles(quantile_levels)
def predict(self, dataset: GluonDataset, **kwargs) -> Iterator[Forecast]:
for entry in dataset:
target = np.asarray(entry["target"], dtype=np.float64)
univariate = target.ndim == 1
working = target.reshape(1, -1) if univariate else target
rows = []
for variate_idx in range(working.shape[0]):
series = working[variate_idx]
series = series[np.isfinite(series)]
if series.size == 0:
series = np.zeros(1, dtype=np.float64)
rows.append(self.quantile_forecast_arrays(series))
stacked = np.stack(rows, axis=0)
forecast_arrays = stacked[0] if univariate else stacked
yield QuantileForecast(
forecast_arrays=forecast_arrays,
forecast_keys=self.config.forecast_keys,
start_date=entry["start"] + target.shape[-1],
item_id=entry["item_id"],
)
class MovingAveragePredictor(StatisticalBaselinePredictor):
"""Rolling mean baseline with bootstrap quantile intervals."""
def __init__(self, prediction_length: int, window: int = 24, **kwargs):
super().__init__(prediction_length=prediction_length, **kwargs)
self.window = max(1, int(window))
def point_forecast_1d(self, history: np.ndarray) -> np.ndarray:
window = history[-min(self.window, history.size):]
value = float(np.nanmean(window)) if window.size else 0.0
return np.full(self.prediction_length, value, dtype=np.float64)
class ARIMAPredictor(StatisticalBaselinePredictor):
"""ARIMA baseline using statsmodels, with residual bootstrap quantiles."""
def __init__(self, prediction_length: int, order: tuple[int, int, int] = (1, 1, 1), **kwargs):
super().__init__(prediction_length=prediction_length, **kwargs)
self.order = order
self._last_fitted: np.ndarray | None = None
def point_forecast_1d(self, history: np.ndarray) -> np.ndarray:
self._last_fitted = None
if history.size < 8 or np.nanstd(history) == 0:
return np.full(self.prediction_length, float(history[-1]), dtype=np.float64)
try:
from statsmodels.tsa.arima.model import ARIMA
fit = ARIMA(history, order=self.order).fit()
fitted = np.asarray(fit.fittedvalues, dtype=np.float64)
if fitted.size == history.size:
self._last_fitted = fitted
forecast = np.asarray(fit.forecast(steps=self.prediction_length), dtype=np.float64)
if forecast.size == self.prediction_length and np.all(np.isfinite(forecast)):
return forecast
except Exception:
pass
return np.full(self.prediction_length, float(history[-1]), dtype=np.float64)
def residuals_1d(self, history: np.ndarray, fitted: np.ndarray | None = None) -> np.ndarray:
return super().residuals_1d(history, self._last_fitted)
class ETSPredictor(StatisticalBaselinePredictor):
"""Exponential smoothing baseline with residual bootstrap quantiles."""
def point_forecast_1d(self, history: np.ndarray) -> np.ndarray:
if history.size < 4 or np.nanstd(history) == 0:
return np.full(self.prediction_length, float(history[-1]), dtype=np.float64)
try:
from statsmodels.tsa.holtwinters import ExponentialSmoothing
trend = "add" if history.size >= 8 else None
fit = ExponentialSmoothing(
history,
trend=trend,
seasonal=None,
initialization_method="estimated",
).fit(optimized=True)
forecast = np.asarray(fit.forecast(self.prediction_length), dtype=np.float64)
if forecast.size == self.prediction_length and np.all(np.isfinite(forecast)):
return forecast
except Exception:
pass
return np.full(self.prediction_length, float(history[-1]), dtype=np.float64)
|