# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file is distributed
# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied. See the License for the specific language governing
# permissions and limitations under the License.
from typing import Optional
import numpy as np
from gluonts.core.component import validated
from gluonts.dataset.common import DataEntry, Dataset
from gluonts.dataset.field_names import FieldName
from gluonts.dataset.util import forecast_start
from gluonts.model.estimator import Estimator
from gluonts.model.forecast import SampleForecast
from gluonts.model.predictor import RepresentablePredictor
from gluonts.model.trivial.constant import ConstantPredictor
from gluonts.pydantic import PositiveInt
[docs]class MeanPredictor(RepresentablePredictor):
"""
A :class:`Predictor` that predicts the samples based on the mean of the
last `context_length` elements of the input target.
Parameters
----------
context_length
Length of the target context used to condition the predictions.
prediction_length
Length of the prediction horizon.
num_samples
Number of samples to use to construct :class:`SampleForecast` objects
for every prediction.
"""
@validated()
def __init__(
self,
prediction_length: int,
num_samples: int = 100,
context_length: Optional[int] = None,
) -> None:
super().__init__(prediction_length=prediction_length)
self.context_length = context_length
self.num_samples = num_samples
self.shape = (self.num_samples, self.prediction_length)
[docs] def predict_item(self, item: DataEntry) -> SampleForecast:
if self.context_length is not None:
target = item["target"][-self.context_length :]
else:
target = item["target"]
mean = np.nanmean(target)
std = np.nanstd(target)
normal = np.random.standard_normal(self.shape)
return SampleForecast(
samples=std * normal + mean,
start_date=forecast_start(item),
item_id=item.get(FieldName.ITEM_ID),
)
[docs]class MovingAveragePredictor(RepresentablePredictor):
"""
A :class:`Predictor` that predicts the moving average based on the last
`context_length` elements of the input target.
If `prediction_length` = 1, the output is the moving average
based on the last `context_length` elements of the input target.
If `prediction_length` > 1, the output is the moving average based on the
last `context_length` elements of the input target, where previously
calculated moving averages are appended at the end of the inputtarget.
Hence, for `prediction_length` larger than `context_length`, there will be
cases where the moving average is calculated on top of previous moving
averages.
Parameters
----------
context_length
Length of the target context used to condition the predictions.
prediction_length
Length of the prediction horizon.
"""
@validated()
def __init__(
self,
prediction_length: int,
context_length: Optional[int] = None,
) -> None:
super().__init__(prediction_length=prediction_length)
if context_length is not None:
assert (
context_length >= 1
), "The value of 'context_length' should be >= 1 or None"
self.context_length = context_length
[docs] def predict_item(self, item: DataEntry) -> SampleForecast:
target = item["target"].tolist()
for _ in range(self.prediction_length):
if self.context_length is not None:
window = target[-self.context_length :]
else:
window = target
target.append(np.nanmean(window))
return SampleForecast(
samples=np.array([target[-self.prediction_length :]]),
start_date=forecast_start(item),
item_id=item.get(FieldName.ITEM_ID),
)
[docs]class MeanEstimator(Estimator):
"""
An `Estimator` that computes the mean targets in the training data, in the
trailing `prediction_length` observations, and produces a
`ConstantPredictor` that always predicts such mean value.
Parameters
----------
prediction_length
Prediction horizon.
num_samples
Number of samples to include in the forecasts. Not that the samples
produced by this predictor will all be identical.
"""
@validated()
def __init__(
self,
prediction_length: PositiveInt,
num_samples: PositiveInt,
) -> None:
super().__init__()
self.prediction_length = prediction_length
self.num_samples = num_samples
[docs] def train(
self,
training_data: Dataset,
validation_dataset: Optional[Dataset] = None,
) -> ConstantPredictor:
contexts = np.array(
[
item["target"][-self.prediction_length :]
for item in training_data
]
)
samples = np.broadcast_to(
array=contexts.mean(axis=0),
shape=(self.num_samples, self.prediction_length),
)
return ConstantPredictor(samples=samples)