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Functions768 in github.com/thuml/Time-Series-Library

⨍ Functions 768◇ Types & classes 221
Method__init__
(self, configs, seg_num, factor, d_model, n_heads, d_ff=None, dropout=0.1)
layers/SelfAttention_Family.py:252
Method__init__
(self, sigma)
layers/ETSformer_EncDec.py:11
Method__init__
(self, dim, nhead, dropout=0.1, aux=False)
layers/ETSformer_EncDec.py:48
Method__init__
(self, d_model, dim_feedforward, dropout=0.1, activation='sigmoid')
layers/ETSformer_EncDec.py:89
Method__init__
(self, d_model, nhead, d_head=None, dropout=0.1)
layers/ETSformer_EncDec.py:105
Method__init__
(self, d_model, pred_len, k=None, low_freq=1)
layers/ETSformer_EncDec.py:135
Method__init__
(self, d_model, c_out, dropout=0.1)
layers/ETSformer_EncDec.py:184
Method__init__
(self, d_model, nhead, c_out, seq_len, pred_len, k, dim_feedforward=None, dropout=0.1, activa
layers/ETSformer_EncDec.py:207
Method__init__
(self, pred_len, nhead, dropout=0.1)
layers/ETSformer_EncDec.py:268
Method__init__
(self, d_model, nhead, c_out, pred_len, dropout=0.1)
layers/ETSformer_EncDec.py:294
Method__init__
(self, layers)
layers/ETSformer_EncDec.py:314
Method__init__
(self, c_in)
layers/Transformer_EncDec.py:7
Method__init__
(self, attention, d_model, d_ff=None, dropout=0.1, activation="relu")
layers/Transformer_EncDec.py:28
Method__init__
(self, self_attention, cross_attention, d_model, d_ff=None, dropout=0.1, activation="relu")
layers/Transformer_EncDec.py:84
Method__init__
(self, layers, norm_layer=None, projection=None)
layers/Transformer_EncDec.py:120
Method__init__
:param num_features: the number of features or channels :param eps: a value added for numerical stability :param affine: if T
layers/StandardNorm.py:6
Method__init__
(self, correlation, d_model, n_heads, d_keys=None, d_values=None)
layers/AutoCorrelation.py:132
Method__init__
(self, args)
exp/exp_classification.py:17
Method__init__
(self, args)
exp/exp_long_term_forecasting.py:19
Method__init__
(self, args)
exp/exp_short_term_forecasting.py:20
Method__init__
(self, model_map)
exp/exp_basic.py:83
Method__init__
(self, args)
exp/exp_anomaly_detection.py:21
Method__init__
(self, args)
exp/exp_zero_shot_forecasting.py:19
Method__init__
(self, args)
exp/exp_imputation.py:17
Method__init__
Args: norm_type: choose from: "standardization", "minmax": normalizes dataframe across ALL contained rows (time s
data_provider/uea.py:63
Method__init__
(self, args, root_path, flag='train', size=None, features='S', data_path='ETTh1.csv',
data_provider/data_loader.py:22
Method__init__
(self, args, root_path, flag='train', size=None, features='S', data_path='ETTm1.csv',
data_provider/data_loader.py:122
Method__init__
(self, args, root_path, flag='train', size=None, features='S', data_path='ETTh1.csv',
data_provider/data_loader.py:224
Method__init__
(self, args, root_path, flag='pred', size=None, features='S', data_path='ETTh1.csv',
data_provider/data_loader.py:334
Method__init__
(self, args, root_path, win_size, step=1, flag="train")
data_provider/data_loader.py:413
Method__init__
(self, args, root_path, win_size, step=1, flag="train")
data_provider/data_loader.py:475
Method__init__
(self, args, root_path, win_size, step=1, flag="train")
data_provider/data_loader.py:538
Method__init__
(self, args, root_path, win_size, step=100, flag="train")
data_provider/data_loader.py:603
Method__init__
(self, args, root_path, win_size, step=1, flag="train")
data_provider/data_loader.py:663
Method__init__
(self, args, root_path, file_list=None, limit_size=None, flag=None)
data_provider/data_loader.py:738
Method__init__
(self, configs)
models/Transformer.py:17
Method__init__
N: the order of the HiPPO projection dt: discretization step size - should be roughly inverse to the length of the sequence
models/FiLM.py:21
Method__init__
1D Fourier layer. It does FFT, linear transform, and Inverse FFT.
models/FiLM.py:60
Method__init__
patch_len: int, patch len for patch_embedding stride: int, stride for patch_embedding
models/Chronos.py:10
Method__init__
(self, top_k: int = 5)
models/TimeMixer.py:14
Method__init__
(self, configs)
models/TimeMixer.py:34
Method__init__
(self, configs)
models/TimeMixer.py:78
Method__init__
(self, configs)
models/TimeMixer.py:119
Method__init__
(self, d_model, embed_type='fixed', freq='h')
models/TemporalFusionTransformer.py:33
Method__init__
(self, d_model, embed_type='timeF', freq='h')
models/TemporalFusionTransformer.py:51
Method__init__
(self, configs)
models/TemporalFusionTransformer.py:61
Method__init__
(self, input_size, output_size)
models/TemporalFusionTransformer.py:106
Method__init__
(self, input_size, output_size, hidden_size=None, context_size=None, dropout=0.0)
models/TemporalFusionTransformer.py:119
Method__init__
(self, d_model, variable_num, dropout=0.0)
models/TemporalFusionTransformer.py:141
Method__init__
(self, d_model, static_len, dropout=0.0)
models/TemporalFusionTransformer.py:162
Method__init__
(self, configs)
models/TemporalFusionTransformer.py:177
Method__init__
(self, configs)
models/TemporalFusionTransformer.py:211
Method__init__
(self, configs)
models/TemporalFusionTransformer.py:255
Method__init__
(self, configs)
models/TimesNet.py:22
Method__init__
(self, input_seq=[], batch_size=[], channel=[], pred_seq=[], dropout=[], factor=[], d_model=[])
models/WPMixer.py:16
Method__init__
(self, input_seq=[], pred_seq=[], batch_size=[],
models/WPMixer.py:93
Method__init__
(self, input_length=[], pred_length=[], wavelet_name=[],
models/WPMixer.py:174
Method__init__
(self, args, tfactor=5, dfactor=5, wavelet='db2', level=1, stride=8, no_decomposition=False)
models/WPMixer.py:273
Method__init__
(self, dim, patch_len, stride=None, pos=True)
models/TimeFilter.py:12
Method__init__
(self, configs)
models/ETSformer.py:12
Method__init__
patch_len: int, patch len for patch_embedding stride: int, stride for patch_embedding
models/TimesFM.py:10
Method__init__
(self, window: int, order: int, *args, **kwargs)
models/KANAD.py:8
Method__init__
(self, configs)
models/iTransformer.py:15
Method__init__
patch_len: int, patch len for patch_embedding stride: int, stride for patch_embedding
models/Moirai.py:13
Method__init__
(self, configs)
models/SegRNN.py:12
Method__init__
patch_len: int, patch len for patch_embedding stride: int, stride for patch_embedding
models/Sundial.py:9
Method__init__
(self, mask_spectrum)
models/Koopa.py:12
Method__init__
(self)
models/Koopa.py:70
Method__init__
(self)
models/Koopa.py:108
Method__init__
(self, enc_in=8, input_len=96, pred_len=96,
models/Koopa.py:156
Method__init__
(self, input_len=96, pred_len=96, dynamic_dim=128,
models/Koopa.py:208
Method__init__
mask_spectrum: list, shared frequency spectrums seg_len: int, segment length of time series dynamic_dim: int, latent dimensio
models/Koopa.py:241
Method__init__
(self, configs)
models/TSMixer.py:5
Method__init__
(self, configs, patch_len=16, stride=8)
models/PAttn.py:12
Method__init__
individual: Bool, whether shared model among different variates.
models/DLinear.py:12
Method__init__
patch_len: int, patch len for patch_embedding stride: int, stride for patch_embedding
models/TimeMoE.py:9
Method__init__
(self, d_model: int, d_hidden: int = 512)
models/MultiPatchFormer.py:10
Method__init__
( self, d_model: int, mha: AttentionLayer, d_hidden: int, dropout: flo
models/MultiPatchFormer.py:26
Method__init__
(self, configs)
models/Informer.py:15
Method__init__
(self, configs)
models/FreTS.py:12
Method__init__
(self, configs)
models/Crossformer.py:18
Method__init__
(self, configs, d_inner, dt_rank)
models/MambaSimple.py:56
Method__init__
(self, configs, d_inner, dt_rank)
models/MambaSimple.py:67
Method__init__
(self, d_model, eps=1e-5)
models/MambaSimple.py:155
Method__init__
(self, enc_in, seq_len, hidden_dims, hidden_layers, output_dim, kernel_size=3)
models/Nonstationary_Transformer.py:15
Method__init__
version: str, for FEDformer, there are two versions to choose, options: [Fourier, Wavelets]. mode_select: str, for FEDformer, there a
models/FEDformer.py:17
Method__init__
patch_len: int, patch len for patch_embedding stride: int, stride for patch_embedding
models/TiRex.py:10
Method__init__
(self, input_dim, hid_dim, output_dim, num_node)
models/LightTS.py:7
Method__init__
(self, *dims, contiguous=False)
models/PatchTST.py:8
Method__init__
(self, n_vars, nf, target_window, head_dropout=0)
models/PatchTST.py:17
Method__init__
(self, embedding_size=512, n_heads=8, dropout=0.05, d_layers=1, decomp_kernel=[32], c_out=1,
models/MICN.py:91
Method__init__
conv_kernel: downsampling and upsampling convolution kernel_size
models/MICN.py:112
Method__init__
(self, configs)
models/MSGNet.py:23
Method__init__
bucket_size: int, n_hashes: int,
models/Reformer.py:15
Method__init__
(self)
models/SCINet.py:7
Method__init__
(self, d_model, kernel_size=5, dropout=0.0)
models/SCINet.py:22
Method__init__
(self, d_model, kernel_size=5, dropout=0.0)
models/SCINet.py:43
Method__init__
(self, d_model, current_level=3, kernel_size=5, dropout=0.0)
models/SCINet.py:63
Method__init__
(self, n_vars, nf, target_window, head_dropout=0)
models/TimeXer.py:10
Method__init__
(self, n_vars, d_model, patch_len, dropout)
models/TimeXer.py:25
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