def __init__(
self,
input_size: int,
freq: str,
prediction_length: int,
context_length: Optional[int] = None,
trainer: Trainer = Trainer(),
dropout_rate: float = 0.1,
cardinality: Optional[List[int]] = None,
embedding_dimension: List[int] = [20],
distr_output: DistributionOutput = StudentTOutput(),
d_model: int = 32,
dim_feedforward_scale: int = 4,
act_type: str = "gelu",
num_heads: int = 8,
num_encoder_layers: int = 3,
num_decoder_layers: int = 3,
scaling: bool = True,
lags_seq: Optional[List[int]] = None,
time_features: Optional[List[TimeFeature]] = None,
use_feat_dynamic_real: bool = False,
use_feat_static_cat: bool = False,
use_feat_static_real: bool = False,
num_parallel_samples: int = 100,
) -> None:
super().__init__(trainer=trainer)
self.input_size = input_size
self.freq = freq
self.prediction_length = prediction_length
self.context_length = (context_length if context_length is not None
else prediction_length)
self.distr_output = distr_output
self.dropout_rate = dropout_rate
self.use_feat_dynamic_real = use_feat_dynamic_real
self.use_feat_static_cat = use_feat_static_cat
self.use_feat_static_real = use_feat_static_real
self.cardinality = cardinality if use_feat_static_cat else [1]
self.embedding_dimension = embedding_dimension
self.num_parallel_samples = num_parallel_samples
self.lags_seq = (lags_seq if lags_seq is not None else
lags_for_fourier_time_features_from_frequency(
freq_str=freq))
self.time_features = (time_features if time_features is not None else
fourier_time_features_from_frequency(self.freq))
self.history_length = self.context_length + max(self.lags_seq)
self.scaling = scaling
self.d_model = d_model
self.num_heads = num_heads
self.act_type = act_type
self.dim_feedforward_scale = dim_feedforward_scale
self.num_encoder_layers = num_encoder_layers
self.num_decoder_layers = num_decoder_layers
self.train_sampler = ExpectedNumInstanceSampler(
num_instances=1.0, min_future=prediction_length)
self.validation_sampler = ValidationSplitSampler(
min_future=prediction_length)
def __init__(
self,
input_size: int,
freq: str,
prediction_length: int,
target_dim: int,
trainer: Trainer = Trainer(),
context_length: Optional[int] = None,
num_layers: int = 2,
num_cells: int = 40,
cell_type: str = "LSTM",
num_parallel_samples: int = 100,
dropout_rate: float = 0.1,
cardinality: List[int] = [1],
embedding_dimension: int = 5,
conditioning_length: int = 100,
diff_steps: int = 100,
loss_type: str = "l2",
beta_end=0.1,
beta_schedule="linear",
residual_layers=8,
residual_channels=8,
dilation_cycle_length=2,
scaling: bool = True,
pick_incomplete: bool = False,
lags_seq: Optional[List[int]] = None,
time_features: Optional[List[TimeFeature]] = None,
**kwargs,
) -> None:
super().__init__(trainer=trainer, **kwargs)
self.freq = freq
self.context_length = (context_length if context_length is not None
else prediction_length)
self.input_size = input_size
self.prediction_length = prediction_length
self.target_dim = target_dim
self.num_layers = num_layers
self.num_cells = num_cells
self.cell_type = cell_type
self.num_parallel_samples = num_parallel_samples
self.dropout_rate = dropout_rate
self.cardinality = cardinality
self.embedding_dimension = embedding_dimension
self.conditioning_length = conditioning_length
self.diff_steps = diff_steps
self.loss_type = loss_type
self.beta_end = beta_end
self.beta_schedule = beta_schedule
self.residual_layers = residual_layers
self.residual_channels = residual_channels
self.dilation_cycle_length = dilation_cycle_length
self.lags_seq = (lags_seq if lags_seq is not None else
lags_for_fourier_time_features_from_frequency(
freq_str=freq))
self.time_features = (time_features if time_features is not None else
fourier_time_features_from_frequency(self.freq))
self.history_length = self.context_length + max(self.lags_seq)
self.pick_incomplete = pick_incomplete
self.scaling = scaling
self.train_sampler = ExpectedNumInstanceSampler(
num_instances=1.0,
min_past=0 if pick_incomplete else self.history_length,
min_future=prediction_length,
)
self.validation_sampler = ValidationSplitSampler(
min_past=0 if pick_incomplete else self.history_length,
min_future=prediction_length,
)
def __init__(
self,
input_size: int,
freq: str,
prediction_length: int,
target_dim: int,
trainer: Trainer = Trainer(),
context_length: Optional[int] = None,
num_layers: int = 2,
num_cells: int = 40,
cell_type: str = "LSTM",
num_parallel_samples: int = 100,
dropout_rate: float = 0.1,
cardinality: List[int] = [1],
embedding_dimension: int = 5,
flow_type="RealNVP",
n_blocks=3,
hidden_size=100,
n_hidden=2,
conditioning_length: int = 200,
dequantize: bool = False,
scaling: bool = True,
pick_incomplete: bool = False,
lags_seq: Optional[List[int]] = None,
time_features: Optional[List[TimeFeature]] = None,
**kwargs,
) -> None:
super().__init__(trainer=trainer, **kwargs)
self.freq = freq
self.context_length = (context_length if context_length is not None
else prediction_length)
self.input_size = input_size
self.prediction_length = prediction_length
self.target_dim = target_dim
self.num_layers = num_layers
self.num_cells = num_cells
self.cell_type = cell_type
self.num_parallel_samples = num_parallel_samples
self.dropout_rate = dropout_rate
self.cardinality = cardinality
self.embedding_dimension = embedding_dimension
self.flow_type = flow_type
self.n_blocks = n_blocks
self.hidden_size = hidden_size
self.n_hidden = n_hidden
self.conditioning_length = conditioning_length
self.dequantize = dequantize
self.lags_seq = (lags_seq if lags_seq is not None else
lags_for_fourier_time_features_from_frequency(
freq_str=freq))
self.time_features = (time_features if time_features is not None else
fourier_time_features_from_frequency(self.freq))
self.history_length = self.context_length + max(self.lags_seq)
self.pick_incomplete = pick_incomplete
self.scaling = scaling
self.train_sampler = ExpectedNumInstanceSampler(
num_instances=1.0,
min_past=0 if pick_incomplete else self.history_length,
min_future=prediction_length,
)
self.validation_sampler = ValidationSplitSampler(
min_past=0 if pick_incomplete else self.history_length,
min_future=prediction_length,
)
def __init__(
self,
input_size: int,
freq: str,
prediction_length: int,
target_dim: int,
trainer: Trainer = Trainer(),
context_length: Optional[int] = None,
num_layers: int = 2,
num_cells: int = 40,
cell_type: str = "LSTM",
num_parallel_samples: int = 100,
dropout_rate: float = 0.1,
use_feat_dynamic_real: bool = False,
use_feat_static_cat: bool = False,
use_feat_static_real: bool = False,
cardinality: Optional[List[int]] = None,
embedding_dimension: Optional[List[int]] = None,
distr_output: Optional[DistributionOutput] = None,
rank: Optional[int] = 5,
scaling: bool = True,
pick_incomplete: bool = False,
lags_seq: Optional[List[int]] = None,
time_features: Optional[List[TimeFeature]] = None,
conditioning_length: int = 200,
use_marginal_transformation=False,
**kwargs,
) -> None:
super().__init__(trainer=trainer, **kwargs)
self.freq = freq
self.context_length = (context_length if context_length is not None
else prediction_length)
if distr_output is not None:
self.distr_output = distr_output
else:
self.distr_output = LowRankMultivariateNormalOutput(dim=target_dim,
rank=rank)
self.input_size = input_size
self.prediction_length = prediction_length
self.target_dim = target_dim
self.num_layers = num_layers
self.num_cells = num_cells
self.cell_type = cell_type
self.num_parallel_samples = num_parallel_samples
self.dropout_rate = dropout_rate
self.use_feat_dynamic_real = use_feat_dynamic_real
self.use_feat_static_cat = use_feat_static_cat
self.use_feat_static_real = use_feat_static_real
self.cardinality = cardinality if cardinality and use_feat_static_cat else [
1
]
self.embedding_dimension = (
embedding_dimension if embedding_dimension is not None else
[min(50, (cat + 1) // 2) for cat in self.cardinality])
self.conditioning_length = conditioning_length
self.use_marginal_transformation = use_marginal_transformation
self.lags_seq = (lags_seq if lags_seq is not None else
lags_for_fourier_time_features_from_frequency(
freq_str=freq))
self.time_features = (time_features if time_features is not None else
fourier_time_features_from_frequency(self.freq))
self.history_length = self.context_length + max(self.lags_seq)
self.pick_incomplete = pick_incomplete
self.scaling = scaling
if self.use_marginal_transformation:
self.output_transform: Optional[
Callable] = cdf_to_gaussian_forward_transform
else:
self.output_transform = None
self.train_sampler = ExpectedNumInstanceSampler(
num_instances=1.0,
min_past=0 if pick_incomplete else self.history_length,
min_future=prediction_length,
)
self.validation_sampler = ValidationSplitSampler(
min_past=0 if pick_incomplete else self.history_length,
min_future=prediction_length,
)
def __init__(
self,
input_size: int,
freq: str,
prediction_length: int,
target_dim: int,
trainer: Trainer = Trainer(),
context_length: Optional[int] = None,
d_model: int = 32,
dim_feedforward_scale: int = 4,
act_type: str = "gelu",
num_heads: int = 8,
num_encoder_layers: int = 3,
num_decoder_layers: int = 3,
num_parallel_samples: int = 100,
dropout_rate: float = 0.1,
use_feat_dynamic_real: bool = False,
flow_type="RealNVP",
n_blocks=3,
hidden_size=100,
n_hidden=2,
conditioning_length: int = 200,
dequantize: bool = False,
scaling: bool = True,
pick_incomplete: bool = False,
lags_seq: Optional[List[int]] = None,
time_features: Optional[List[TimeFeature]] = None,
**kwargs,
) -> None:
super().__init__(trainer=trainer, **kwargs)
self.freq = freq
self.context_length = (context_length if context_length is not None
else prediction_length)
self.input_size = input_size
self.prediction_length = prediction_length
self.target_dim = target_dim
self.d_model = d_model
self.num_heads = num_heads
self.act_type = act_type
self.dim_feedforward_scale = dim_feedforward_scale
self.num_encoder_layers = num_encoder_layers
self.num_decoder_layers = num_decoder_layers
self.num_parallel_samples = num_parallel_samples
self.dropout_rate = dropout_rate
self.use_feat_dynamic_real = use_feat_dynamic_real
self.flow_type = flow_type
self.n_blocks = n_blocks
self.hidden_size = hidden_size
self.n_hidden = n_hidden
self.conditioning_length = conditioning_length
self.dequantize = dequantize
self.lags_seq = (lags_seq if lags_seq is not None else
lags_for_fourier_time_features_from_frequency(
freq_str=freq))
self.time_features = (time_features if time_features is not None else
fourier_time_features_from_frequency(self.freq))
self.history_length = self.context_length + max(self.lags_seq)
self.pick_incomplete = pick_incomplete
self.scaling = scaling
self.train_sampler = ExpectedNumInstanceSampler(
num_instances=1.0,
min_past=0 if pick_incomplete else self.history_length,
min_future=prediction_length,
)
self.validation_sampler = ValidationSplitSampler(
min_past=0 if pick_incomplete else self.history_length,
min_future=prediction_length,
)
