def dsinfo(request):
from gluonts import time_feature
from gluonts.dataset.artificial import constant_dataset, default_synthetic
if request.param == "constant":
ds_info, train_ds, test_ds = constant_dataset()
return AttrDict(
name="constant",
cardinality=int(ds_info.metadata.feat_static_cat[0].cardinality),
freq=ds_info.metadata.freq,
num_parallel_samples=2,
prediction_length=ds_info.prediction_length,
# FIXME: Should time features should not be needed for GP
time_features=[time_feature.DayOfWeek(),
time_feature.HourOfDay()],
train_ds=train_ds,
test_ds=test_ds,
)
elif request.param == "synthetic":
ds_info, train_ds, test_ds = default_synthetic()
return AttrDict(
name="synthetic",
batch_size=32,
cardinality=int(ds_info.metadata.feat_static_cat[0].cardinality),
context_length=2,
freq=ds_info.metadata.freq,
prediction_length=ds_info.prediction_length,
num_parallel_samples=2,
train_ds=train_ds,
test_ds=test_ds,
time_features=None,
)
from gluonts.model.gp_forecaster import GaussianProcessEstimator
from gluonts.model.predictor import Predictor
from gluonts.model.seasonal_naive import SeasonalNaiveEstimator
from gluonts.model.seq2seq import (
MQCNNEstimator,
MQRNNEstimator,
Seq2SeqEstimator,
)
from gluonts.model.simple_feedforward import SimpleFeedForwardEstimator
dataset_info, train_ds, test_ds = constant_dataset()
freq = dataset_info.metadata.time_granularity
prediction_length = dataset_info.prediction_length
cardinality = int(dataset_info.metadata.feat_static_cat[0].cardinality)
# FIXME: Should time features should not be needed for GP
time_features = [time_feature.DayOfWeek(), time_feature.HourOfDay()]
num_eval_samples = 2
epochs = 1
def seq2seq_base(seq2seq_model, hybridize: bool = True, batches_per_epoch=1):
return (
seq2seq_model,
dict(
ctx='cpu',
epochs=epochs,
learning_rate=1e-2,
hybridize=hybridize,
prediction_length=prediction_length,
context_length=prediction_length,
num_eval_samples=num_eval_samples,
