def __init__(
self,
freq: str,
prediction_length: int,
input_size: int,
trainer: Trainer = Trainer(),
context_length: Optional[int] = None,
num_layers: int = 3,
num_cells: int = 40,
cell_type: str = "LSTM",
dropout_rate: float = 0.1,
use_feat_dynamic_real: bool = False,
use_feat_dynamic_cat: 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: DistributionOutput = StudentTOutput(),
scaling: bool = True,
lags_seq: Optional[List[int]] = None,
time_features: Optional[List[TimeFeature]] = None,
num_parallel_samples: int = 100,
dtype: np.dtype = np.float32,
) -> None:
super().__init__(trainer=trainer)
self.freq = freq
self.context_length = (context_length if context_length is not None
else prediction_length)
self.prediction_length = prediction_length
self.distr_output = distr_output
self.distr_output.dtype = dtype
self.input_size = input_size
self.num_layers = num_layers
self.num_cells = num_cells
self.cell_type = cell_type
self.dropout_rate = dropout_rate
self.use_feat_dynamic_real = use_feat_dynamic_real
self.use_feat_dynamic_cat = use_feat_dynamic_cat
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.scaling = scaling
self.lags_seq = (
# lags_seq if lags_seq is not None else get_lags_for_frequency(freq_str=freq)
lags_seq if lags_seq is not None else get_lags_for_frequency(
freq_str=freq, num_lags=0))
self.time_features = (time_features if time_features is not None else
time_features_from_frequency_str(self.freq))
# self.history_length = self.context_length + max(self.lags_seq)
self.history_length = self.context_length
self.num_parallel_samples = num_parallel_samples
def test_lags():
freq_strs = [
"min",
"1min",
"15min",
"30min",
"59min",
"60min",
"61min",
"H",
"1H",
"6H",
"12H",
"23H",
"24H",
"25H",
"D",
"1D",
"2D",
"6D",
"7D",
"8D",
"W",
"1W",
"3W",
"4W",
"5W",
"M",
"6M",
"12M",
]
for freq_str in freq_strs:
lags = get_lags_for_frequency(freq_str)
assert (
lags == expected_lags[freq_str]
), "lags do not match for the frequency '{}':\nexpected: {},\nprovided: {}".format(
freq_str, expected_lags[freq_str], lags
)