update baselines comments (#152)

* update more baselines

* update baselines comments

baselines/ETSformer/arch/etsformer_arch.py

@@ -26,11 +26,13 @@ def shift(self, x):
 
 
 class ETSformer(nn.Module):
-
+    """
+    Paper: ETSformer: Exponential Smoothing Transformers for Time-series Forecasting
+    Official Code: https://github.com/salesforce/ETSformer
+    Link: https://arxiv.org/abs/2202.01381
+    """
     def __init__(self,  **model_args):
         super().__init__()
-
-
         self.seq_len = model_args['seq_len']
         self.pred_len = model_args['pred_len']
         self.e_layers = model_args['e_layers']

baselines/FiLM/arch/film_arch.py

@@ -90,7 +90,9 @@ def forward(self, x):
 
 class FiLM(nn.Module):
     """
-    Paper link: https://arxiv.org/abs/2205.08897
+    Paper: FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting
+    Official Code: https://github.com/tianzhou2011/FiLM
+    Link: https://arxiv.org/abs/2205.08897
     """
     def __init__(self, **model_args):
         super(FiLM, self).__init__()

baselines/Koopa/arch/koopa_arch.py

@@ -234,9 +234,11 @@ def forward(self, x):
 
 
 class Koopa(nn.Module):
-    '''
-    Koopman Forecasting Model
-    '''
+    """
+    Paper: Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors
+    Official Code: https://github.com/thuml/Koopa
+    Link: https://arxiv.org/abs/2305.18803
+    """
     def __init__(self, **model_args):
         super(Koopa, self).__init__()
         self.mask_spectrum = None  # 初始化为None

baselines/LightTS/arch/lightts_arch.py

@@ -38,7 +38,9 @@ def forward(self, x):
 
 class LightTS(nn.Module):
     """
-    Paper link: https://arxiv.org/abs/2207.01186
+    Paper: Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP
+    UnOfficial Code: https://github.com/thuml/Time-Series-Library/blob/main/models/LightTS.py
+    Link: https://arxiv.org/abs/2207.01186
     """
 
     def __init__(self, **model_args):

baselines/MTSMixer/arch/mtsmixer_arch.py

@@ -78,6 +78,11 @@ def forward(self,x):
 
 
 class MTSMixer(nn.Module):
+    """
+    Paper: MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixing
+    Official Code: https://github.com/plumprc/MTS-Mixers
+    Link: https://arxiv.org/abs/2302.04501
+    """
     def __init__(self,  **model_args):
         super(MTSMixer, self).__init__()
         self.pred_len = model_args['pred_len']

baselines/Nonstationary_Transformer/arch/nstransformer_arch.py

@@ -38,9 +38,10 @@ def forward(self, x, stats):
 
 class Nonstationary_Transformer(nn.Module):
     """
-    Non-stationary Transformer
+    Paper: Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting
+    Official Code: https://github.com/thuml/Nonstationary_Transformers
+    Link: https://arxiv.org/abs/2205.14415
     """
-
     def __init__(self, **model_args):
         super(Nonstationary_Transformer, self).__init__()
         self.pred_len = model_args['pred_len']

baselines/SegRNN/arch/segrnn_arch.py

@@ -6,9 +6,10 @@
 
 class SegRNN(nn.Module):
     """
-    Paper link: https://arxiv.org/abs/2308.11200.pdf
+    Paper: SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting
+    Official Code: https://github.com/lss-1138/SegRNN
+    Link: https://arxiv.org/abs/2308.11200
     """
-
     def __init__(self, **model_args):
         super(SegRNN, self).__init__()
 

baselines/SparseTSF/arch/sparsetsf_arch.py

@@ -3,6 +3,11 @@
 from .Embed import PositionalEmbedding
 
 class SparseTSF(nn.Module):
+    """
+    Paper: SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters
+    Official Code: https://github.com/lss-1138/SparseTSF
+    Link: https://arxiv.org/abs/2405.00946
+    """
     def __init__(self, **model_args):
         super(SparseTSF, self).__init__()
 

baselines/TiDE/arch/tide_arch.py

@@ -39,7 +39,9 @@ def forward(self, x):
 # TiDE
 class TiDE(nn.Module):
     """
-    paper: https://arxiv.org/pdf/2304.08424.pdf
+    Paper: Long-term Forecasting with TiDE: Time-series Dense Encoder
+    Official Code: https://github.com/lich99/TiDE
+    Link: https://arxiv.org/abs/2304.08424
     """
 
     def __init__(self,  **model_args):

baselines/TimeMixer/arch/timemixer_arch.py

@@ -196,7 +196,11 @@ def forward(self, x_list):
 
 
 class TimeMixer(nn.Module):
-
+    """
+    Paper: TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting
+    Official Code: https://github.com/kwuking/TimeMixer
+    Link: https://arxiv.org/html/2405.14616v1
+    """
     def __init__(self, **model_args):
         super(TimeMixer, self).__init__()
 

baselines/UMixer/arch/umixer_arch.py

@@ -148,6 +148,11 @@ def forward(self, x):
 
 
 class UMixer(nn.Module):
+    """
+    Paper: U-Mixer: An Unet-Mixer Architecture with Stationarity Correction for Time Series Forecasting
+    Official Code: https://github.com/XiangMa-Shaun/U-Mixer
+    Link: https://arxiv.org/abs/2401.02236
+    """
     def __init__(self,  **model_args):
         super(UMixer, self).__init__()
         self.pred_len = model_args['pred_len']

baselines/iTransformer/arch/itransformer_arch.py

@@ -9,9 +9,10 @@
 
 class iTransformer(nn.Module):
     """
-    Paper link: https://arxiv.org/abs/2310.06625
+    Paper: iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
+    Official Code: https://github.com/thuml/iTransformer
+    Link: https://arxiv.org/abs/2310.06625
     """
-
     def __init__(self, **model_args):
         super(iTransformer, self).__init__()
         self.pred_len = model_args['pred_len']