def deep_state(seed=42, data="m4_quarterly", epochs=100, batches=50):
mx.random.seed(seed)
np.random.seed(seed)
dataset = get_dataset(data)
trainer = Trainer(
ctx=mx.cpu(0),
# ctx=mx.gpu(0),
epochs=epochs,
num_batches_per_epoch=batches,
learning_rate=1e-3,
)
cardinality = int(dataset.metadata.feat_static_cat[0].cardinality)
estimator = DeepStateEstimator(
trainer=trainer,
cardinality=[cardinality],
prediction_length=dataset.metadata.prediction_length,
freq=dataset.metadata.freq,
use_feat_static_cat=True,
)
predictor = estimator.train(dataset.train)
# predictor = estimator.train(training_data=dataset.train,
# validation_data=dataset.test)
forecast_it, ts_it = make_evaluation_predictions(dataset.test,
predictor=predictor,
num_samples=100)
agg_metrics, item_metrics = Evaluator()(ts_it,
forecast_it,
num_series=len(dataset.test))
metrics = [
"MASE", "sMAPE", "MSIS", "wQuantileLoss[0.5]", "wQuantileLoss[0.9]"
]
output = {
key: round(value, 8)
for key, value in agg_metrics.items() if key in metrics
}
output["epochs"] = epochs
output["seed"] = seed
df = pd.DataFrame([output])
return df
def get_estimator(self, metadata):
self.estimator = DeepStateEstimator(
freq=self.configs.freq,
prediction_length=self.configs.pred_len,
cardinality=[
3 if self.configs.six_ramps else 1
], ##one per sequence type 1)demand ramp 2) solar ramp 3) wind ramp
issm=OurSeasonality.get_seasonality(self.configs),
use_feat_static_cat=True if self.configs.six_ramps else False,
add_trend=True,
past_length=self.configs.context_len,
num_layers=self.configs.num_layers,
num_cells=self.configs.num_hidden,
trainer=Trainer(ctx=self.ctx,
epochs=self.configs.num_epochs,
learning_rate=self.configs.learning_rate,
hybridize=False,
patience=5,
num_batches_per_epoch=self.configs.train_len //
self.configs.batch_size,
batch_size=self.configs.batch_size))
return self.estimator
def train(args):
freq = args.freq.replace('"', '')
prediction_length = args.prediction_length
context_length = args.context_length
use_feat_dynamic_real = args.use_feat_dynamic_real
use_past_feat_dynamic_real = args.use_past_feat_dynamic_real
use_feat_static_cat = args.use_feat_static_cat
use_log1p = args.use_log1p
print('freq:', freq)
print('prediction_length:', prediction_length)
print('context_length:', context_length)
print('use_feat_dynamic_real:', use_feat_dynamic_real)
print('use_past_feat_dynamic_real:', use_past_feat_dynamic_real)
print('use_feat_static_cat:', use_feat_static_cat)
print('use_log1p:', use_log1p)
batch_size = args.batch_size
print('batch_size:', batch_size)
train = load_json(os.path.join(args.train, 'train_'+freq+'.json'))
test = load_json(os.path.join(args.test, 'test_'+freq+'.json'))
num_timeseries = len(train)
print('num_timeseries:', num_timeseries)
train_ds = ListDataset(parse_data(train, use_log1p=use_log1p), freq=freq)
test_ds = ListDataset(parse_data(test, use_log1p=use_log1p), freq=freq)
predictor = None
trainer= Trainer(ctx="cpu",
epochs=args.epochs,
num_batches_per_epoch=args.num_batches_per_epoch,
learning_rate=args.learning_rate,
learning_rate_decay_factor=args.learning_rate_decay_factor,
patience=args.patience,
minimum_learning_rate=args.minimum_learning_rate,
clip_gradient=args.clip_gradient,
weight_decay=args.weight_decay,
init=args.init.replace('"', ''),
hybridize=args.hybridize)
print('trainer:', trainer)
cardinality = None
if args.cardinality != '':
cardinality = args.cardinality.replace('"', '').replace(' ', '').replace('[', '').replace(']', '').split(',')
for i in range(len(cardinality)):
cardinality[i] = int(cardinality[i])
print('cardinality:', cardinality)
embedding_dimension = [min(50, (cat+1)//2) for cat in cardinality] if cardinality is not None else None
print('embedding_dimension:', embedding_dimension)
algo_name = args.algo_name.replace('"', '')
print('algo_name:', algo_name)
if algo_name == 'CanonicalRNN':
estimator = CanonicalRNNEstimator(
freq=freq,
prediction_length=prediction_length,
context_length=context_length,
trainer=trainer,
batch_size=batch_size,
num_layers=5,
num_cells=50,
cell_type='lstm',
num_parallel_samples=100,
cardinality=cardinality,
embedding_dimension=10,
)
elif algo_name == 'DeepFactor':
estimator = DeepFactorEstimator(
freq=freq,
prediction_length=prediction_length,
context_length=context_length,
trainer=trainer,
batch_size=batch_size,
cardinality=cardinality,
embedding_dimension=10,
)
elif algo_name == 'DeepAR':
estimator = DeepAREstimator(
freq = freq, # – Frequency of the data to train on and predict
prediction_length = prediction_length, # – Length of the prediction horizon
trainer = trainer, # – Trainer object to be used (default: Trainer())
context_length = context_length, # – Number of steps to unroll the RNN for before computing predictions (default: None, in which case context_length = prediction_length)
num_layers = 2, # – Number of RNN layers (default: 2)
num_cells = 40, # – Number of RNN cells for each layer (default: 40)
cell_type = 'lstm', # – Type of recurrent cells to use (available: ‘lstm’ or ‘gru’; default: ‘lstm’)
dropoutcell_type = 'ZoneoutCell', # – Type of dropout cells to use (available: ‘ZoneoutCell’, ‘RNNZoneoutCell’, ‘VariationalDropoutCell’ or ‘VariationalZoneoutCell’; default: ‘ZoneoutCell’)
dropout_rate = 0.1, # – Dropout regularization parameter (default: 0.1)
use_feat_dynamic_real = use_feat_dynamic_real, # – Whether to use the feat_dynamic_real field from the data (default: False)
use_feat_static_cat = use_feat_static_cat, # – Whether to use the feat_static_cat field from the data (default: False)
use_feat_static_real = False, # – Whether to use the feat_static_real field from the data (default: False)
cardinality = cardinality, # – Number of values of each categorical feature. This must be set if use_feat_static_cat == True (default: None)
embedding_dimension = embedding_dimension, # – Dimension of the embeddings for categorical features (default: [min(50, (cat+1)//2) for cat in cardinality])
# distr_output = StudentTOutput(), # – Distribution to use to evaluate observations and sample predictions (default: StudentTOutput())
# scaling = True, # – Whether to automatically scale the target values (default: true)
# lags_seq = None, # – Indices of the lagged target values to use as inputs of the RNN (default: None, in which case these are automatically determined based on freq)
# time_features = None, # – Time features to use as inputs of the RNN (default: None, in which case these are automatically determined based on freq)
# num_parallel_samples = 100, # – Number of evaluation samples per time series to increase parallelism during inference. This is a model optimization that does not affect the accuracy (default: 100)
# imputation_method = None, # – One of the methods from ImputationStrategy
# train_sampler = None, # – Controls the sampling of windows during training.
# validation_sampler = None, # – Controls the sampling of windows during validation.
# alpha = None, # – The scaling coefficient of the activation regularization
# beta = None, # – The scaling coefficient of the temporal activation regularization
batch_size = batch_size, # – The size of the batches to be used training and prediction.
# minimum_scale = None, # – The minimum scale that is returned by the MeanScaler
# default_scale = None, # – Default scale that is applied if the context length window is completely unobserved. If not set, the scale in this case will be the mean scale in the batch.
# impute_missing_values = None, # – Whether to impute the missing values during training by using the current model parameters. Recommended if the dataset contains many missing values. However, this is a lot slower than the default mode.
# num_imputation_samples = None, # – How many samples to use to impute values when impute_missing_values=True
)
elif algo_name == 'DeepState':
estimator = DeepStateEstimator(
freq=freq,
prediction_length=prediction_length,
trainer=trainer,
batch_size=batch_size,
use_feat_dynamic_real=use_feat_dynamic_real,
use_feat_static_cat=use_feat_static_cat,
cardinality=cardinality,
)
elif algo_name == 'DeepVAR':
estimator = DeepVAREstimator( # use multi
freq=freq,
prediction_length=prediction_length,
context_length=context_length,
trainer=trainer,
batch_size=batch_size,
target_dim=96,
)
elif algo_name == 'GaussianProcess':
# # TODO
# estimator = GaussianProcessEstimator(
# freq=freq,
# prediction_length=prediction_length,
# context_length=context_length,
# trainer=trainer,
# batch_size=batch_size,
# cardinality=num_timeseries,
# )
pass
elif algo_name == 'GPVAR':
estimator = GPVAREstimator( # use multi
freq=freq,
prediction_length=prediction_length,
context_length=context_length,
trainer=trainer,
batch_size=batch_size,
target_dim=96,
)
elif algo_name == 'LSTNet':
estimator = LSTNetEstimator( # use multi
freq=freq,
prediction_length=prediction_length,
context_length=context_length,
num_series=96,
skip_size=4,
ar_window=4,
channels=72,
trainer=trainer,
batch_size=batch_size,
)
elif algo_name == 'NBEATS':
estimator = NBEATSEstimator(
freq=freq,
prediction_length=prediction_length,
context_length=context_length,
trainer=trainer,
batch_size=batch_size,
)
elif algo_name == 'DeepRenewalProcess':
estimator = DeepRenewalProcessEstimator(
freq=freq,
prediction_length=prediction_length,
context_length=context_length,
trainer=trainer,
batch_size=batch_size,
num_cells=40,
num_layers=2,
)
elif algo_name == 'Tree':
estimator = TreePredictor(
freq = freq,
prediction_length = prediction_length,
context_length = context_length,
n_ignore_last = 0,
lead_time = 0,
max_n_datapts = 1000000,
min_bin_size = 100, # Used only for "QRX" method.
use_feat_static_real = False,
use_feat_dynamic_cat = False,
use_feat_dynamic_real = use_feat_dynamic_real,
cardinality = cardinality,
one_hot_encode = False,
model_params = {'eta': 0.1, 'max_depth': 6, 'silent': 0, 'nthread': -1, 'n_jobs': -1, 'gamma': 1, 'subsample': 0.9, 'min_child_weight': 1, 'colsample_bytree': 0.9, 'lambda': 1, 'booster': 'gbtree'},
max_workers = 4, # default: None
method = "QRX", # "QRX", "QuantileRegression", "QRF"
quantiles=None, # Used only for "QuantileRegression" method.
model=None,
seed=None,
)
elif algo_name == 'SelfAttention':
# # TODO
# estimator = SelfAttentionEstimator(
# freq=freq,
# prediction_length=prediction_length,
# context_length=context_length,
# trainer=trainer,
# batch_size=batch_size,
# )
pass
elif algo_name == 'MQCNN':
estimator = MQCNNEstimator(
freq=freq,
prediction_length=prediction_length,
context_length=context_length,
trainer=trainer,
batch_size=batch_size,
use_past_feat_dynamic_real=use_past_feat_dynamic_real,
use_feat_dynamic_real=use_feat_dynamic_real,
use_feat_static_cat=use_feat_static_cat,
cardinality=cardinality,
embedding_dimension=embedding_dimension,
add_time_feature=True,
add_age_feature=False,
enable_encoder_dynamic_feature=True,
enable_decoder_dynamic_feature=True,
seed=None,
decoder_mlp_dim_seq=None,
channels_seq=None,
dilation_seq=None,
kernel_size_seq=None,
use_residual=True,
quantiles=None,
distr_output=None,
scaling=None,
scaling_decoder_dynamic_feature=False,
num_forking=None,
max_ts_len=None,
)
elif algo_name == 'MQRNN':
estimator = MQRNNEstimator(
freq=freq,
prediction_length=prediction_length,
context_length=context_length,
trainer=trainer,
batch_size=batch_size,
)
elif algo_name == 'Seq2Seq':
# # TODO
# estimator = Seq2SeqEstimator(
# freq=freq,
# prediction_length=prediction_length,
# context_length=context_length,
# trainer=trainer,
# cardinality=cardinality,
# embedding_dimension=4,
# encoder=Seq2SeqEncoder(),
# decoder_mlp_layer=[4],
# decoder_mlp_static_dim=4
# )
pass
elif algo_name == 'SimpleFeedForward':
estimator = SimpleFeedForwardEstimator(
num_hidden_dimensions=[40, 40],
prediction_length=prediction_length,
context_length=context_length,
freq=freq,
trainer=trainer,
batch_size=batch_size,
)
elif algo_name == 'TemporalFusionTransformer':
estimator = TemporalFusionTransformerEstimator(
prediction_length=prediction_length,
context_length=context_length,
freq=freq,
trainer=trainer,
batch_size=batch_size,
hidden_dim = 32,
variable_dim = None,
num_heads = 4,
num_outputs = 3,
num_instance_per_series = 100,
dropout_rate = 0.1,
# time_features = [],
# static_cardinalities = {},
# dynamic_cardinalities = {},
# static_feature_dims = {},
# dynamic_feature_dims = {},
# past_dynamic_features = []
)
elif algo_name == 'DeepTPP':
# # TODO
# estimator = DeepTPPEstimator(
# prediction_interval_length=prediction_length,
# context_interval_length=context_length,
# freq=freq,
# trainer=trainer,
# batch_size=batch_size,
# num_marks=len(cardinality) if cardinality is not None else 0,
# )
pass
elif algo_name == 'Transformer':
estimator = TransformerEstimator(
freq=freq,
prediction_length=prediction_length,
trainer=trainer,
batch_size=batch_size,
cardinality=cardinality,
)
elif algo_name == 'WaveNet':
estimator = WaveNetEstimator(
freq=freq,
prediction_length=prediction_length,
trainer=trainer,
batch_size=batch_size,
cardinality=cardinality,
)
elif algo_name == 'Naive2':
# TODO Multiplicative seasonality is not appropriate for zero and negative values
predictor = Naive2Predictor(freq=freq, prediction_length=prediction_length, season_length=context_length)
elif algo_name == 'NPTS':
predictor = NPTSPredictor(freq=freq, prediction_length=prediction_length, context_length=context_length)
elif algo_name == 'Prophet':
def configure_model(model):
model.add_seasonality(
name='weekly', period=7, fourier_order=3, prior_scale=0.1
)
return model
predictor = ProphetPredictor(freq=freq, prediction_length=prediction_length, init_model=configure_model)
elif algo_name == 'ARIMA':
predictor = RForecastPredictor(freq=freq,
prediction_length=prediction_length,
method_name='arima',
period=context_length,
trunc_length=len(train[0]['target']))
elif algo_name == 'ETS':
predictor = RForecastPredictor(freq=freq,
prediction_length=prediction_length,
method_name='ets',
period=context_length,
trunc_length=len(train[0]['target']))
elif algo_name == 'TBATS':
predictor = RForecastPredictor(freq=freq,
prediction_length=prediction_length,
method_name='tbats',
period=context_length,
trunc_length=len(train[0]['target']))
elif algo_name == 'CROSTON':
predictor = RForecastPredictor(freq=freq,
prediction_length=prediction_length,
method_name='croston',
period=context_length,
trunc_length=len(train[0]['target']))
elif algo_name == 'MLP':
predictor = RForecastPredictor(freq=freq,
prediction_length=prediction_length,
method_name='mlp',
period=context_length,
trunc_length=len(train[0]['target']))
elif algo_name == 'SeasonalNaive':
predictor = SeasonalNaivePredictor(freq=freq, prediction_length=prediction_length)
else:
print('[ERROR]:', algo_name, 'not supported')
return
if predictor is None:
try:
predictor = estimator.train(train_ds, test_ds)
except Exception as e:
print(e)
try:
grouper_train = MultivariateGrouper(max_target_dim=num_timeseries)
train_ds_multi = grouper_train(train_ds)
test_ds_multi = grouper_train(test_ds)
predictor = estimator.train(train_ds_multi, test_ds_multi)
except Exception as e:
print(e)
forecast_it, ts_it = make_evaluation_predictions(
dataset=test_ds, # test dataset
predictor=predictor, # predictor
num_samples=100, # number of sample paths we want for evaluation
)
forecasts = list(forecast_it)
tss = list(ts_it)
# print(len(forecasts), len(tss))
evaluator = Evaluator(quantiles=[0.1, 0.5, 0.9])
agg_metrics, item_metrics = evaluator(iter(tss), iter(forecasts), num_series=len(test_ds))
print(json.dumps(agg_metrics, indent=4))
model_dir = os.path.join(args.model_dir, algo_name)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
predictor.serialize(Path(model_dir))
def fit(self, df, future_regressor=[]):
"""Train algorithm given data supplied.
Args:
df (pandas.DataFrame): Datetime Indexed
"""
df = self.basic_profile(df)
try:
from mxnet.random import seed as mxnet_seed
mxnet_seed(self.random_seed)
except Exception:
pass
gluon_train = df.transpose()
self.train_index = gluon_train.index
gluon_freq = str(self.frequency).split('-')[0]
if gluon_freq in ["MS", "1MS"]:
gluon_freq = "M"
if int(self.verbose) > 1:
print(f"Gluon Frequency is {gluon_freq}")
if str(self.context_length).replace('.', '').isdigit():
self.gluon_context_length = int(float(self.context_length))
elif 'forecastlength' in str(self.context_length).lower():
len_int = int([x for x in str(self.context_length)
if x.isdigit()][0])
self.gluon_context_length = int(len_int * self.forecast_length)
else:
self.gluon_context_length = 2 * self.forecast_length
self.context_length = '2ForecastLength'
ts_metadata = {
'num_series':
len(gluon_train.index),
'freq':
gluon_freq,
'gluon_start':
[gluon_train.columns[0] for _ in range(len(gluon_train.index))],
'context_length':
self.gluon_context_length,
'forecast_length':
self.forecast_length,
}
self.test_ds = ListDataset(
[{
FieldName.TARGET: target,
FieldName.START: start
}
for (target,
start) in zip(gluon_train.values, ts_metadata['gluon_start'])
],
freq=ts_metadata['freq'],
)
if self.gluon_model == 'DeepAR':
from gluonts.model.deepar import DeepAREstimator
estimator = DeepAREstimator(
freq=ts_metadata['freq'],
context_length=ts_metadata['context_length'],
prediction_length=ts_metadata['forecast_length'],
trainer=Trainer(epochs=self.epochs,
learning_rate=self.learning_rate),
)
elif self.gluon_model == 'NPTS':
from gluonts.model.npts import NPTSEstimator
estimator = NPTSEstimator(
freq=ts_metadata['freq'],
context_length=ts_metadata['context_length'],
prediction_length=ts_metadata['forecast_length'],
)
elif self.gluon_model == 'MQCNN':
from gluonts.model.seq2seq import MQCNNEstimator
estimator = MQCNNEstimator(
freq=ts_metadata['freq'],
context_length=ts_metadata['context_length'],
prediction_length=ts_metadata['forecast_length'],
trainer=Trainer(epochs=self.epochs,
learning_rate=self.learning_rate),
)
elif self.gluon_model == 'SFF':
from gluonts.model.simple_feedforward import SimpleFeedForwardEstimator
estimator = SimpleFeedForwardEstimator(
prediction_length=ts_metadata['forecast_length'],
context_length=ts_metadata['context_length'],
freq=ts_metadata['freq'],
trainer=Trainer(
epochs=self.epochs,
learning_rate=self.learning_rate,
hybridize=False,
num_batches_per_epoch=100,
),
)
elif self.gluon_model == 'Transformer':
from gluonts.model.transformer import TransformerEstimator
estimator = TransformerEstimator(
prediction_length=ts_metadata['forecast_length'],
context_length=ts_metadata['context_length'],
freq=ts_metadata['freq'],
trainer=Trainer(epochs=self.epochs,
learning_rate=self.learning_rate),
)
elif self.gluon_model == 'DeepState':
from gluonts.model.deepstate import DeepStateEstimator
estimator = DeepStateEstimator(
prediction_length=ts_metadata['forecast_length'],
past_length=ts_metadata['context_length'],
freq=ts_metadata['freq'],
use_feat_static_cat=False,
cardinality=[1],
trainer=Trainer(ctx='cpu',
epochs=self.epochs,
learning_rate=self.learning_rate),
)
elif self.gluon_model == 'DeepFactor':
from gluonts.model.deep_factor import DeepFactorEstimator
estimator = DeepFactorEstimator(
freq=ts_metadata['freq'],
context_length=ts_metadata['context_length'],
prediction_length=ts_metadata['forecast_length'],
trainer=Trainer(epochs=self.epochs,
learning_rate=self.learning_rate),
)
elif self.gluon_model == 'WaveNet':
# Usually needs more epochs/training iterations than other models do
from gluonts.model.wavenet import WaveNetEstimator
estimator = WaveNetEstimator(
freq=ts_metadata['freq'],
prediction_length=ts_metadata['forecast_length'],
trainer=Trainer(epochs=self.epochs,
learning_rate=self.learning_rate),
)
else:
raise ValueError("'gluon_model' not recognized.")
self.GluonPredictor = estimator.train(self.test_ds)
self.ts_metadata = ts_metadata
self.fit_runtime = datetime.datetime.now() - self.startTime
return self
def fit(self, df, future_regressor=None):
"""Train algorithm given data supplied.
Args:
df (pandas.DataFrame): Datetime Indexed
"""
if not _has_gluonts:
raise ImportError(
"GluonTS installation not found or installed version is incompatible with AutoTS."
)
df = self.basic_profile(df)
try:
from mxnet.random import seed as mxnet_seed
mxnet_seed(self.random_seed)
except Exception:
pass
gluon_train = df.to_numpy().T
self.train_index = df.columns
self.train_columns = df.index
gluon_freq = str(self.frequency).split('-')[0]
if self.regression_type == "User":
if future_regressor is None:
raise ValueError(
"regression_type='User' but no future_regressor supplied")
if gluon_freq in ["MS", "1MS"]:
gluon_freq = "M"
if int(self.verbose) > 1:
print(f"Gluon Frequency is {gluon_freq}")
if int(self.verbose) < 1:
try:
logging.getLogger().disabled = True
logging.getLogger("mxnet").addFilter(lambda record: False)
except Exception:
pass
if str(self.context_length).replace('.', '').isdigit():
self.gluon_context_length = int(float(self.context_length))
elif 'forecastlength' in str(self.context_length).lower():
len_int = int([x for x in str(self.context_length)
if x.isdigit()][0])
self.gluon_context_length = int(len_int * self.forecast_length)
else:
self.gluon_context_length = 20
self.context_length = '20'
ts_metadata = {
'num_series':
len(self.train_index),
'freq':
gluon_freq,
'start_ts':
df.index[0],
'gluon_start':
[self.train_columns[0] for _ in range(len(self.train_index))],
'context_length':
self.gluon_context_length,
'forecast_length':
self.forecast_length,
}
if self.gluon_model in self.multivariate_mods:
if self.regression_type == "User":
regr = future_regressor.to_numpy().T
self.regr_train = regr
self.test_ds = ListDataset(
[{
"start": df.index[0],
"target": gluon_train,
"feat_dynamic_real": regr,
}],
freq=ts_metadata['freq'],
one_dim_target=False,
)
else:
self.test_ds = ListDataset(
[{
"start": df.index[0],
"target": gluon_train
}],
freq=ts_metadata['freq'],
one_dim_target=False,
)
else:
if self.regression_type == "User":
self.gluon_train = gluon_train
regr = future_regressor.to_numpy().T
self.regr_train = regr
self.test_ds = ListDataset(
[{
FieldName.TARGET: target,
FieldName.START: ts_metadata['start_ts'],
FieldName.FEAT_DYNAMIC_REAL: regr,
} for target in gluon_train],
freq=ts_metadata['freq'],
)
else:
# use the actual start date, if NaN given (semi-hidden)
# ts_metadata['gluon_start'] = df.notna().idxmax().tolist()
self.test_ds = ListDataset(
[{
FieldName.TARGET: target,
FieldName.START: start
} for (target, start
) in zip(gluon_train, ts_metadata['gluon_start'])],
freq=ts_metadata['freq'],
)
if self.gluon_model == 'DeepAR':
from gluonts.model.deepar import DeepAREstimator
estimator = DeepAREstimator(
freq=ts_metadata['freq'],
context_length=ts_metadata['context_length'],
prediction_length=ts_metadata['forecast_length'],
trainer=Trainer(epochs=self.epochs,
learning_rate=self.learning_rate),
)
elif self.gluon_model == 'NPTS':
from gluonts.model.npts import NPTSEstimator
estimator = NPTSEstimator(
freq=ts_metadata['freq'],
context_length=ts_metadata['context_length'],
prediction_length=ts_metadata['forecast_length'],
)
elif self.gluon_model == 'MQCNN':
from gluonts.model.seq2seq import MQCNNEstimator
estimator = MQCNNEstimator(
freq=ts_metadata['freq'],
context_length=ts_metadata['context_length'],
prediction_length=ts_metadata['forecast_length'],
trainer=Trainer(epochs=self.epochs,
learning_rate=self.learning_rate),
)
elif self.gluon_model == 'SFF':
from gluonts.model.simple_feedforward import SimpleFeedForwardEstimator
estimator = SimpleFeedForwardEstimator(
prediction_length=ts_metadata['forecast_length'],
context_length=ts_metadata['context_length'],
freq=ts_metadata['freq'],
trainer=Trainer(
epochs=self.epochs,
learning_rate=self.learning_rate,
hybridize=False,
num_batches_per_epoch=100,
),
)
elif self.gluon_model == 'Transformer':
from gluonts.model.transformer import TransformerEstimator
estimator = TransformerEstimator(
prediction_length=ts_metadata['forecast_length'],
context_length=ts_metadata['context_length'],
freq=ts_metadata['freq'],
trainer=Trainer(epochs=self.epochs,
learning_rate=self.learning_rate),
)
elif self.gluon_model == 'DeepState':
from gluonts.model.deepstate import DeepStateEstimator
estimator = DeepStateEstimator(
prediction_length=ts_metadata['forecast_length'],
past_length=ts_metadata['context_length'],
freq=ts_metadata['freq'],
use_feat_static_cat=False,
cardinality=[1],
trainer=Trainer(ctx='cpu',
epochs=self.epochs,
learning_rate=self.learning_rate),
)
elif self.gluon_model == 'DeepFactor':
from gluonts.model.deep_factor import DeepFactorEstimator
estimator = DeepFactorEstimator(
freq=ts_metadata['freq'],
context_length=ts_metadata['context_length'],
prediction_length=ts_metadata['forecast_length'],
trainer=Trainer(epochs=self.epochs,
learning_rate=self.learning_rate),
)
elif self.gluon_model == 'WaveNet':
# Usually needs more epochs/training iterations than other models do
from gluonts.model.wavenet import WaveNetEstimator
estimator = WaveNetEstimator(
freq=ts_metadata['freq'],
prediction_length=ts_metadata['forecast_length'],
trainer=Trainer(epochs=self.epochs,
learning_rate=self.learning_rate),
)
elif self.gluon_model == 'DeepVAR':
from gluonts.model.deepvar import DeepVAREstimator
estimator = DeepVAREstimator(
target_dim=gluon_train.shape[0],
freq=ts_metadata['freq'],
context_length=ts_metadata['context_length'],
prediction_length=ts_metadata['forecast_length'],
trainer=Trainer(epochs=self.epochs,
learning_rate=self.learning_rate),
)
elif self.gluon_model == 'GPVAR':
from gluonts.model.gpvar import GPVAREstimator
estimator = GPVAREstimator(
target_dim=gluon_train.shape[0],
freq=ts_metadata['freq'],
context_length=ts_metadata['context_length'],
prediction_length=ts_metadata['forecast_length'],
trainer=Trainer(epochs=self.epochs,
learning_rate=self.learning_rate),
)
elif self.gluon_model == 'LSTNet':
from gluonts.model.lstnet import LSTNetEstimator
estimator = LSTNetEstimator(
freq=ts_metadata['freq'],
num_series=len(self.train_index),
skip_size=0,
ar_window=1,
channels=2,
context_length=ts_metadata['context_length'],
prediction_length=ts_metadata['forecast_length'],
trainer=Trainer(epochs=self.epochs,
learning_rate=self.learning_rate),
)
elif self.gluon_model == 'NBEATS':
from gluonts.model.n_beats import NBEATSEstimator
estimator = NBEATSEstimator(
freq=ts_metadata['freq'],
context_length=ts_metadata['context_length'],
prediction_length=ts_metadata['forecast_length'],
trainer=Trainer(epochs=self.epochs,
learning_rate=self.learning_rate),
)
elif self.gluon_model == 'Rotbaum':
from gluonts.model.rotbaum import TreeEstimator
estimator = TreeEstimator(
freq=ts_metadata['freq'],
context_length=ts_metadata['context_length'],
prediction_length=ts_metadata['forecast_length'],
# trainer=Trainer(epochs=self.epochs, learning_rate=self.learning_rate),
)
elif self.gluon_model == 'DeepRenewalProcess':
from gluonts.model.renewal import DeepRenewalProcessEstimator
estimator = DeepRenewalProcessEstimator(
prediction_length=ts_metadata['forecast_length'],
context_length=ts_metadata['context_length'],
num_layers=1, # original paper used 1 layer, 10 cells
num_cells=10,
freq=ts_metadata['freq'],
trainer=Trainer(epochs=self.epochs,
learning_rate=self.learning_rate),
)
elif self.gluon_model == 'SelfAttention':
from gluonts.model.san import SelfAttentionEstimator
estimator = SelfAttentionEstimator(
prediction_length=ts_metadata['forecast_length'],
context_length=ts_metadata['context_length'],
freq=ts_metadata['freq'],
trainer=Trainer(
epochs=self.epochs,
learning_rate=self.learning_rate,
use_feature_dynamic_real=False,
),
)
elif self.gluon_model == 'TemporalFusionTransformer':
from gluonts.model.tft import TemporalFusionTransformerEstimator
estimator = TemporalFusionTransformerEstimator(
prediction_length=ts_metadata['forecast_length'],
context_length=ts_metadata['context_length'],
freq=ts_metadata['freq'],
trainer=Trainer(epochs=self.epochs,
learning_rate=self.learning_rate),
)
elif self.gluon_model == 'DeepTPP':
from gluonts.model.tpp.deeptpp import DeepTPPEstimator
estimator = DeepTPPEstimator(
prediction_interval_length=ts_metadata['forecast_length'],
context_interval_length=ts_metadata['context_length'],
num_marks=1, # cardinality
freq=ts_metadata['freq'],
trainer=Trainer(
epochs=self.epochs,
learning_rate=self.learning_rate,
hybridize=False,
),
)
else:
raise ValueError("'gluon_model' not recognized.")
self.GluonPredictor = estimator.train(self.test_ds)
self.ts_metadata = ts_metadata
self.fit_runtime = datetime.datetime.now() - self.startTime
return self