20260310 meeting

Finalizing work v1.0

Published 2026-03-08 Updated 2026-03-09 2799 words 14 minutes views

This experiment re-evaluates the Weather2K, NASA GES DISC MERRA-2, and 2b_8 datasets.

The dataset sizes used are as follows:

Dataset	# of Variables	Known Locations	`batch_size`	Unknown Locations	Training Time Steps	Future Time Steps
Weather2K	4	1,492	100	374	1,176	88
MERRA-2	1	950	100	50	4,608	144
2b_8	1	9,500	200	500	90	10

Experimental Settings

The configurations used in this experiment are as follows:

config.yaml

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wavenet:
  # WaveNet model parameters
  input_channels:  1  # Number of input channels
  output_channels: 1  # Number of output channels
  residual_layers: 32  # Number of residual layers
  residual_channels: 64  # Number of channels in residual blocks
  skip_channels: 64  # Number of channels in skip connections

  # Diffusion step embedding dimensions
  diffusion_step_embed_dim_input:  128  # Input dimension
  diffusion_step_embed_dim_hidden: 256  # Middle dimension
  diffusion_step_embed_dim_output: 256  # Output dimension

  # Structured State Spaces sequence model (S4) configurations
  s4_max_sequence_length: 90  # Maximum sequence length
  s4_state_dim: 128  # State dimension
  s4_dropout: 0.2  # Dropout rate
  s4_bidirectional: true  # Whether to use bidirectional layers
  s4_use_layer_norm: true  # Whether to use layer normalization

diffusion:
  # Diffusion model parameters
  T: 100  # Number of diffusion steps
  beta_0: 0.0001  # Initial beta value
  beta_T: 0.01  # Final beta value

AFRK:
# AutoFRK model parameters, for more details, please refer to https://pypi.org/project/autoFRK/
  method: "fast"  # autoFRK method to use (e.g., "fast")
  tps_method: "rectangular"  # autoFRK's TPS method to use (e.g., "rectangular")

Among them, depending on the dataset, the modified parameters include input_channels, output_channels, and s4_max_sequence_length.

In this experiment, for all datasets the training set was modified to directly impute several future time steps. Tests showed that this approach can significantly improve the accuracy of imputation. At the same time, the impact of s4_bidirectional on imputation was also evaluated.

For the Weather2K dataset, the case with s4_dropout = 0.2 was tested.
For the MERRA-2 dataset, multiple settings were tested with s4_dropout = 0.0, 0.2, and 0.8.
For the 2b_8 dataset, the case with s4_dropout = 0.0 was tested.

In training.yaml, the effect of enabling or disabling enable_spatial_training (AFRK) was evaluated.

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# Training configuration
batch_size: 200  # Batch size
output_directory: "/home/u6025091/SSSD_CP/results/2b_8-fast-rectangular"  # Output directory for checkpoints and logs
ckpt_iter: "max"  # Checkpoint mode (max or min)
iters_per_ckpt: 500  # Checkpoint frequency (number of epochs)
iters_per_logging: 200  # Log frequency (number of iterations)
n_iters: 38000  # Maximum number of iterations
learning_rate: 0.0005  # Learning rate

# Additional training settings
only_generate_missing: true  # Generate missing values only
use_model: 2  # Model to use for training
masking: "forecast"  # Masking strategy for missing values
missing_k: 10  # Number of missing values

# Data paths
data:
  train_path: "/home/u6025091/SSSD_CP/datasets/2b_8/data_train_known_real.npy"  # Path to training data

# autoFRK config
enable_spatial_training: true  # Enable spatial training step
location_path: "/home/u6025091/SSSD_CP/datasets/2b_8/stations_known_locations.npy"  # Path to known locations

Test the impact of enable_spatial_normalization (SP) in inference.yaml.

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# Inference configuration
batch_size: 200  # Batch size for inference
output_directory: "/home/u6025091/SSSD_CP/results/2b_8-fast-rectangular/inference"  # Output directory for inference results
ckpt_path: "/home/u6025091/SSSD_CP/results/2b_8-fast-rectangular"  # Path to checkpoint for inference
trials: 1 # Replications

# Additional training settings
only_generate_missing: true  # Generate missing values only
use_model: 2  # Model to use for training
masking: "forecast"  # Masking strategy for missing values  # inference mode need to fix in the code, or using other masking strategy will be failed
missing_k: 10  # Number of missing values

# Data paths
data:
  test_path: "/home/u6025091/SSSD_CP/datasets/2b_8/data_train_known_real.npy"  # Path to test data

# autoFRK config
enable_spatial_inference: true  # Enable spatial prediction step
enable_spatial_normalization: false  # Enable spatial normalization before and after autoFRK
known_location_path: "/home/u6025091/SSSD_CP/datasets/2b_8/stations_known_locations.npy"  # Path to known locations
unknown_location_path: "/home/u6025091/SSSD_CP/datasets/2b_8/stations_unknown_locations.npy"  # Path to unknown locations

Weather2K

bidirectional-FRK-4000

The experiment has not yet been completed.

bidirectional-NoFRK-4000

Metric	ALL Locs & All Time	Known Locs & All Time	Unknown Locs & All Time	ALL Locs & Future	Known Locs & Future	Unknown Locs & Future	ALL Locs & Past	Known Locs & Past	Unknown Locs & Past
MSPE	9.212197e+00	4.764740e+00	10.829105	8.104409e+01	68.246143	23.431347	3.837021e+00	1.443116e-02	9.886081
RMSPE	3.035160e+00	2.182828e+00	3.290761	9.002449e+00	8.261122	4.840594	1.958832e+00	1.201298e-01	3.144214
MSPE%	1.607277e+10	1.713991e+06	0.178218	6.444603e+10	1.965038	0.405369	1.245300e+10	1.842249e+06	0.161220
RMSPE%	1.267784e+05	1.309195e+03	0.422158	2.538622e+05	1.401798	0.636686	1.115930e+05	1.357295e+03	0.401522
MAPE	8.855900e-01	4.531534e-01	1.857254	5.544696e+00	5.434782	2.891528	5.369495e-01	8.037850e-02	1.779859
MAPE%	4.245987e+08	2.953705e+07	0.041180	1.013968e+09	0.161959	0.064585	3.804962e+08	3.174730e+07	0.039428

NoSP

Metric	ALL Locs & All Time	Known Locs & All Time	Unknown Locs & All Time	ALL Locs & Future	Known Locs & Future	Unknown Locs & Future	ALL Locs & Past	Known Locs & Past	Unknown Locs & Past
MSPE	9.622670e+00	5.138295e+00	13.595236	8.215610e+01	73.610913	26.586258	4.194998e+00	1.449347e-02	12.623119
RMSPE	3.102043e+00	2.266781e+00	3.687172	9.064000e+00	8.579680	5.156186	2.048169e+00	1.203888e-01	3.552903
MSPE%	1.585812e+10	2.447800e+06	0.245626	6.400976e+10	2.199272	0.484240	1.225494e+10	2.630969e+06	0.227770
RMSPE%	1.259290e+05	1.564545e+03	0.495606	2.530015e+05	1.482994	0.695874	1.107020e+05	1.622026e+03	0.477253
MAPE	9.472319e-01	4.569086e-01	2.333831	5.602686e+00	5.487968	3.326226	5.988646e-01	8.043473e-02	2.259571
MAPE%	4.190081e+08	3.820500e+07	0.055416	1.009678e+09	0.163989	0.077277	3.748083e+08	4.106387e+07	0.053780

Nobidirectional-FRK-4000

The experiment has not yet been completed.

Nobidirectional-NoFRK-4000

https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/1150310%20meeting/2kNobidirectionalNoFRK4000log.png

Metric	ALL Locs & All Time	Known Locs & All Time	Unknown Locs & All Time	ALL Locs & Future	Known Locs & Future	Unknown Locs & Future	ALL Locs & Past	Known Locs & Past	Unknown Locs & Past
MSPE	8.557305e+00	4.357313e+00	10.717023	7.167319e+01	62.453320	21.991220	3.834348e+00	9.992905e-03	9.873375
RMSPE	2.925287e+00	2.087418e+00	3.273686	8.466002e+00	7.902741	4.689480	1.958149e+00	9.996452e-02	3.142193
MSPE%	1.532682e+10	4.910454e+06	0.175495	5.342669e+10	1.243336	0.367536	1.247581e+10	5.277903e+06	0.161125
RMSPE%	1.238015e+05	2.215954e+03	0.418921	2.311421e+05	1.115050	0.606248	1.116952e+05	2.297369e+03	0.401403
MAPE	8.404920e-01	4.067417e-01	1.847794	5.013504e+00	4.959049	2.774382	5.282258e-01	6.609286e-02	1.778458
MAPE%	4.162873e+08	5.306461e+07	0.041032	9.481803e+08	0.129175	0.062375	3.764858e+08	5.703544e+07	0.039435

NoSP

Metric	ALL Locs & All Time	Known Locs & All Time	Unknown Locs & All Time	ALL Locs & Future	Known Locs & Future	Unknown Locs & Future	ALL Locs & Past	Known Locs & Past	Unknown Locs & Past
MSPE	8.821512e+00	4.808902e+00	13.022649	7.165436e+01	68.935532	24.962443	4.119734e+00	1.031077e-02	12.129195
RMSPE	2.970103e+00	2.192921e+00	3.608691	8.464890e+00	8.302742	4.996243	2.029713e+00	1.015420e-01	3.482699
MSPE%	1.546526e+10	4.921911e+06	0.228755	5.539211e+10	1.372258	0.442306	1.247754e+10	5.290217e+06	0.212775
RMSPE%	1.243594e+05	2.218538e+03	0.478283	2.353553e+05	1.171434	0.665061	1.117029e+05	2.300047e+03	0.461275
MAPE	8.856481e-01	4.269254e-01	2.242633	5.031184e+00	5.235553	3.147337	5.754380e-01	6.709618e-02	2.174934
MAPE%	4.147628e+08	5.343217e+07	0.052522	9.579761e+08	0.136539	0.073606	3.741141e+08	5.743049e+07	0.050944

MERRA-2

bidirectional-FRK-4000

The experiment has not yet been completed.

bidirectional-NoFRK-4000

Metric	ALL Locs & All Time	Known Locs & All Time	Unknown Locs & All Time	ALL Locs & Future	Known Locs & Future	Unknown Locs & Future	ALL Locs & Past	Known Locs & Past	Unknown Locs & Past
MSPE	0.631161	0.477201	10.280107	10.491658	8.513340	15.152535	0.066237	0.016798	10.000957
RMSPE	0.794456	0.690798	3.206261	3.239083	2.917763	3.892626	0.257365	0.129606	3.162429
MSPE%	0.002294	0.001739	0.035207	0.038344	0.031085	0.054628	0.000228	0.000057	0.034095
RMSPE%	0.047892	0.041698	0.187636	0.195817	0.176309	0.233727	0.015106	0.007581	0.184648
MAPE	0.251795	0.232853	2.906040	2.635178	2.543609	3.155217	0.115247	0.100466	2.891764
MAPE%	0.000897	0.000827	0.010018	0.009595	0.009263	0.011408	0.000399	0.000344	0.009938

https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/1150310%20meeting/MERRA2bidirectionalNoFRK4000SP1.png

NoSP

Metric	ALL Locs & All Time	Known Locs & All Time	Unknown Locs & All Time	ALL Locs & Future	Known Locs & Future	Unknown Locs & Future	ALL Locs & Past	Known Locs & Past	Unknown Locs & Past
MSPE	198236.030804	0.367509	3.879090e+06	158755.391217	6.478062	3.110934e+06	200497.942447	0.017425	3.923099e+06
RMSPE	445.237050	0.606225	1.969541e+03	398.441202	2.545204	1.763784e+03	447.769966	0.132003	1.980682e+03
MSPE%	688.413089	0.001336	1.351371e+04	576.070077	0.023611	1.133382e+04	694.849408	0.000060	1.363860e+04
RMSPE%	26.237627	0.036548	1.162485e+02	24.001460	0.153657	1.064604e+02	26.359996	0.007719	1.167844e+02
MAPE	80.254871	0.219046	1.583713e+03	74.552805	2.250021	1.426400e+03	80.581552	0.102688	1.592725e+03
MAPE%	0.278721	0.000776	5.517933e+00	0.270491	0.008189	5.196684e+00	0.279192	0.000351	5.536338e+00

bidirectional-NoFRK-dropout0.0-4000

Metric	ALL Locs & All Time	Known Locs & All Time	Unknown Locs & All Time	ALL Locs & Future	Known Locs & Future	Unknown Locs & Future	ALL Locs & Past	Known Locs & Past	Unknown Locs & Past
MSPE	0.679363	0.237607	10.461359	11.335908	4.042383	22.427100	0.068832	0.019625	9.775821
RMSPE	0.824235	0.487450	3.234402	3.366884	2.010568	4.735726	0.262358	0.140090	3.126631
MSPE%	0.002458	0.000855	0.035912	0.041218	0.014586	0.080938	0.000238	0.000068	0.033332
RMSPE%	0.049583	0.029233	0.189504	0.203021	0.120771	0.284496	0.015423	0.008239	0.182571
MAPE	0.259026	0.183983	2.926754	2.667272	1.587548	4.105072	0.121054	0.103570	2.859247
MAPE%	0.000923	0.000649	0.010100	0.009693	0.005751	0.014857	0.000421	0.000356	0.009828

NoSP

Metric	ALL Locs & All Time	Known Locs & All Time	Unknown Locs & All Time	ALL Locs & Future	Known Locs & Future	Unknown Locs & Future	ALL Locs & Past	Known Locs & Past	Unknown Locs & Past
MSPE	272856.376224	0.228059	5.315017e+06	242581.732982	3.865768	4.726952e+06	274590.860993	0.019648	5.348708e+06
RMSPE	522.356560	0.477555	2.305432e+03	492.525870	1.966156	2.174155e+03	524.014180	0.140173	2.312727e+03
MSPE%	948.153585	0.000819	1.855650e+04	880.933318	0.013936	1.728082e+04	952.004747	0.000068	1.862959e+04
RMSPE%	30.792103	0.028626	1.362223e+02	29.680521	0.118053	1.314565e+02	30.854574	0.008244	1.364902e+02
MAPE	88.936696	0.181412	1.752376e+03	88.691191	1.539571	1.702171e+03	88.950762	0.103600	1.755252e+03
MAPE%	0.309068	0.000639	6.114465e+00	0.321926	0.005576	6.209867e+00	0.308331	0.000356	6.108999e+00

bidirectional-NoFRK-dropout0.8-4000

Metric	ALL Locs & All Time	Known Locs & All Time	Unknown Locs & All Time	ALL Locs & Future	Known Locs & Future	Unknown Locs & Future	ALL Locs & Past	Known Locs & Past	Unknown Locs & Past
MSPE	1.124980	1.106429	10.146221	19.710693	20.270051	14.558338	0.060173	0.008513	9.893443
RMSPE	1.060651	1.051869	3.185313	4.439673	4.502227	3.815539	0.245302	0.092268	3.145384
MSPE%	0.004122	0.004039	0.034800	0.072441	0.074030	0.053435	0.000207	0.000029	0.033733
RMSPE%	0.064199	0.063555	0.186548	0.269149	0.272084	0.231161	0.014401	0.005416	0.183664
MAPE	0.281098	0.287881	2.894569	3.584130	4.110291	3.182689	0.091862	0.068889	2.878062
MAPE%	0.001010	0.001035	0.009987	0.013090	0.014978	0.011638	0.000318	0.000237	0.009892

NoSP

Metric	ALL Locs & All Time	Known Locs & All Time	Unknown Locs & All Time	ALL Locs & Future	Known Locs & Future	Unknown Locs & Future	ALL Locs & Past	Known Locs & Past	Unknown Locs & Past
MSPE	150574.444572	1.987353	2.942067e+06	133335.662610	36.514928	2.609129e+06	151562.083122	0.009211	2.961141e+06
RMSPE	388.039231	1.409735	1.715245e+03	365.151561	6.042758	1.615280e+03	389.309752	0.095974	1.720797e+03
MSPE%	522.973730	0.007246	1.025620e+04	484.079926	0.133161	9.536985e+03	525.202021	0.000032	1.029740e+04
RMSPE%	22.868619	0.085121	1.012729e+02	22.001816	0.364912	9.765749e+01	22.917287	0.005628	1.014761e+02
MAPE	68.112053	0.367612	1.342040e+03	67.630784	5.516283	1.270001e+03	68.139626	0.072636	1.346168e+03
MAPE%	0.236618	0.001324	4.678681e+00	0.245512	0.020084	4.633175e+00	0.236109	0.000249	4.681288e+00

Nobidirectional-FRK-4000

https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/1150310%20meeting/MERRA2NobidirectionalFRK4000log.png

Metric	ALL Locs & All Time	Known Locs & All Time	Unknown Locs & All Time	ALL Locs & Future	Known Locs & Future	Unknown Locs & Future	ALL Locs & Past	Known Locs & Past	Unknown Locs & Past
MSPE	0.934441	1.179018	9.580757	16.313171	21.751974	8.117971	0.053368	0.000359	9.664563
RMSPE	0.966665	1.085826	3.095280	4.038957	4.663901	2.849205	0.231016	0.018951	3.108788
MSPE%	0.003425	0.004299	0.032767	0.059999	0.079321	0.029589	0.000184	0.000001	0.032949
RMSPE%	0.058523	0.065569	0.181017	0.244947	0.281639	0.172015	0.013556	0.001117	0.181520
MAPE	0.219504	0.243107	2.817106	3.278275	4.273947	2.423857	0.044262	0.012174	2.839636
MAPE%	0.000794	0.000883	0.009709	0.011977	0.015561	0.008829	0.000153	0.000042	0.009760

NoSP

Metric	ALL Locs & All Time	Known Locs & All Time	Unknown Locs & All Time	ALL Locs & Future	Known Locs & Future	Unknown Locs & Future	ALL Locs & Past	Known Locs & Past	Unknown Locs & Past
MSPE	182,103.4469	1.0957	3,553,594.0	167,537.1967	20.2144	3,269,625.0	182,937.9717	0.0004	3,569,864.0
RMSPE	426.7358	1.0468	1,885.0980	409.3131	4.4960	1,808.2100	427.7125	0.0197	1,889.4080
MSPE%	632.8748	0.0040	12,403.5000	608.0813	0.0738	11,929.6600	634.2953	0.0000	12,430.6400
RMSPE%	25.1570	0.0632	111.3710	24.6593	0.2716	109.2230	25.1852	0.0012	111.4928
MAPE	78.4212	0.2340	1,546.8880	78.8324	4.0981	1,497.2510	78.3976	0.0126	1,549.7310
MAPE%	0.2725	0.0009	5.3968	0.2860	0.0149	5.4566	0.2717	0.0000	5.3934

Nobidirectional-NoFRK-4000

https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/1150310%20meeting/MERRA2NobidirectionalNoFRK4000log.png

Metric	ALL Locs & All Time	Known Locs & All Time	Unknown Locs & All Time	ALL Locs & Future	Known Locs & Future	Unknown Locs & Future	ALL Locs & Past	Known Locs & Past	Unknown Locs & Past
MSPE	0.651523	0.535322	10.326410	10.814419	9.535042	15.213563	0.069273	0.019713	10.046417
RMSPE	0.807169	0.731657	3.213473	3.288528	3.087886	3.900457	0.263198	0.140403	3.169608
MSPE%	0.002367	0.001950	0.035351	0.039531	0.034826	0.054848	0.000238	0.000067	0.034234
RMSPE%	0.048657	0.044163	0.188019	0.198824	0.186617	0.234197	0.015437	0.008180	0.185025
MAPE	0.260319	0.250812	2.906929	2.671431	2.760482	3.155861	0.122182	0.107029	2.892667
MAPE%	0.000926	0.000890	0.010019	0.009726	0.010057	0.011410	0.000422	0.000365	0.009940

NoSP

Metric	ALL Locs & All Time	Known Locs & All Time	Unknown Locs & All Time	ALL Locs & Future	Known Locs & Future	Unknown Locs & Future	ALL Locs & Past	Known Locs & Past	Unknown Locs & Past
MSPE	213455.299309	0.402967	4.175011e+06	198345.954454	7.097287	3.881056e+06	214320.938858	0.019438	4.191852e+06
RMSPE	462.012229	0.634797	2.043284e+03	445.360477	2.664073	1.970040e+03	462.948095	0.139420	2.047401e+03
MSPE%	741.021085	0.001465	1.454442e+04	719.588489	0.025888	1.414078e+04	742.248994	0.000066	1.456755e+04
RMSPE%	27.221702	0.038279	1.206003e+02	26.825147	0.160899	1.189150e+02	27.244247	0.008126	1.206961e+02
MAPE	81.883167	0.229040	1.614716e+03	81.173912	2.366525	1.555511e+03	81.923801	0.106580	1.618108e+03
MAPE%	0.284439	0.000810	5.628739e+00	0.294478	0.008618	5.665761e+00	0.283864	0.000363	5.626618e+00

Nobidirectional-NoFRK-10000

Metric	ALL Locs & All Time	Known Locs & All Time	Unknown Locs & All Time	ALL Locs & Future	Known Locs & Future	Unknown Locs & Future	ALL Locs & Past	Known Locs & Past	Unknown Locs & Past
MSPE	0.635422	0.484750	10.496749	10.497099	8.552034	17.694556	0.070430	0.022561	10.084375
RMSPE	0.797134	0.696240	3.239869	3.239923	2.924386	4.206490	0.265387	0.150205	3.175590
MSPE%	0.002305	0.001766	0.035962	0.038305	0.031243	0.063775	0.000243	0.000077	0.034369
RMSPE%	0.048014	0.042025	0.189637	0.195716	0.176757	0.252537	0.015583	0.008794	0.185388
MAPE	0.260637	0.249688	2.906929	2.616535	2.604560	3.449520	0.125664	0.114774	2.901471
MAPE%	0.000926	0.000886	0.010019	0.009519	0.009490	0.012471	0.000435	0.000393	0.009971

NoSP

Metric	ALL Locs & All Time	Known Locs & All Time	Unknown Locs & All Time	ALL Locs & Future	Known Locs & Future	Unknown Locs & Future	ALL Locs & Past	Known Locs & Past	Unknown Locs & Past
MSPE	181860.883779	0.452924	3.535570e+06	166326.115224	7.964999	3.232551e+06	182750.896561	0.022545	3.552931e+06
RMSPE	426.451502	0.672996	1.880311e+03	407.830989	2.822233	1.797930e+03	427.493739	0.150149	1.884922e+03
MSPE%	634.310666	0.001650	1.238971e+04	604.564722	0.029095	1.181983e+04	636.014861	0.000077	1.242236e+04
RMSPE%	25.185525	0.040616	1.113091e+02	24.587898	0.170572	1.087190e+02	25.219335	0.008791	1.114556e+02
MAPE	76.138943	0.243951	1.499120e+03	75.044246	2.497090	1.432609e+03	76.201660	0.114865	1.502931e+03
MAPE%	0.265154	0.000865	5.242707e+00	0.272554	0.009097	5.229755e+00	0.264730	0.000393	5.243449e+00

2b_8

bidirectional-FRK-s4_dropout0.0-4000

https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/1150310%20meeting/2b_8bidirectionalFRK4000log.png

Metric	ALL Locs & All Time	Known Locs & All Time	Unknown Locs & All Time	ALL Locs & Future	Known Locs & Future	Unknown Locs & Future	ALL Locs & Past	Known Locs & Past	Unknown Locs & Past
MSPE	5.8231e+66	0.1026	1.1646e+68	1.0083e+66	1.0260	2.0166e+67	6.3581e+66	0.000005	1.2716e+68
RMSPE	2.4131e+33	0.3203	1.0792e+34	1.0041e+33	1.0129	4.4906e+33	2.5215e+33	0.002124	1.1277e+34
MSPE%	1.4416e+73	0.1900	2.8833e+74	6.9926e+66	1.8993	1.3985e+68	1.6018e+73	0.000051	3.2036e+74
RMSPE%	3.7969e+36	0.4359	1.6980e+37	2.6443e+33	1.3782	1.1826e+34	4.0023e+36	0.007167	1.7899e+37
MAPE	3.6612e+32	0.0819	7.3224e+33	1.5208e+32	0.8097	3.0416e+33	3.8990e+32	0.000991	7.7980e+33
MAPE%	3.7969e+39	0.3450	7.5937e+40	1.0044e+33	3.3521	2.0088e+34	4.2187e+39	0.010899	8.4375e+40

https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/1150310%20meeting/2b_8bidirectionalFRK4000SP1.png

NoSP

Metric	ALL Locs & All Time	Known Locs & All Time	Unknown Locs & All Time	ALL Locs & Future	Known Locs & Future	Unknown Locs & Future	ALL Locs & Past	Known Locs & Past	Unknown Locs & Past
MSPE	5.823092e+66	0.102603	1.164618e+68	1.008290e+66	1.025985	2.016580e+67	6.358069e+66	0.000005	1.271614e+68
RMSPE	2.413108e+33	0.320316	1.079175e+34	1.004136e+33	1.012909	4.490635e+33	2.521521e+33	0.002124	1.127659e+34
MSPE%	1.441628e+73	0.189981	2.883256e+74	6.992550e+66	1.899346	1.398510e+68	1.601809e+73	0.000051	3.203617e+74
RMSPE%	3.796877e+36	0.435868	1.698015e+37	2.644343e+33	1.378168	1.182586e+34	4.002260e+36	0.007167	1.789865e+37
MAPE	3.661201e+32	0.081864	7.322402e+33	1.520802e+32	0.809727	3.041605e+33	3.899023e+32	0.000991	7.798046e+33
MAPE%	3.796872e+39	0.345023	7.5937e+40	1.004424e+33	3.352141	2.008849e+34	4.218747e+39	0.010899	8.437494e+40

https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/1150310%20meeting/2b_8bidirectionalFRK4000NoSP1.png

bidirectional-NoFRK-s4_dropout0.0-4000

https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/1150310%20meeting/2b_8bidirectionalNoFRK4000log.png

Metric	ALL Locs & All Time	Known Locs & All Time	Unknown Locs & All Time	ALL Locs & Future	Known Locs & Future	Unknown Locs & Future	ALL Locs & Past	Known Locs & Past	Unknown Locs & Past
MSPE	5.147689e+66	0.100314	1.029538e+68	2.716749e+65	0.994832	5.433498e+66	5.689469e+66	0.000923	1.137894e+68
RMSPE	2.268852e+33	0.316724	1.014661e+34	5.212244e+32	0.997413	2.330987e+33	2.385261e+33	0.030387	1.066721e+34
MSPE%	6.448289e+72	0.178323	1.289658e+74	1.978870e+66	1.743544	3.957740e+67	7.164765e+72	0.004410	1.432953e+74
RMSPE%	2.539348e+36	0.422283	1.135631e+37	1.406723e+33	1.320433	6.291057e+33	2.676708e+36	0.066404	1.197060e+37
MAPE	3.230288e+32	0.103238	6.460575e+33	7.896310e+31	0.797468	1.579262e+33	3.501472e+32	0.026101	7.002943e+33
MAPE%	2.539343e+39	0.486568	5.078687e+40	5.159410e+32	3.351772	1.031882e+34	2.821493e+39	0.168212	5.642985e+40

NoSP

Metric	ALL Locs & All Time	Known Locs & All Time	Unknown Locs & All Time	ALL Locs & Future	Known Locs & Future	Unknown Locs & Future	ALL Locs & Past	Known Locs & Past	Unknown Locs & Past
MSPE	5.888744e+66	0.100352	1.177749e+68	5.335852e+65	0.995214	1.067170e+67	6.483761e+66	0.000923	1.296752e+68
RMSPE	2.426673e+33	0.316785	1.085241e+34	7.304692e+32	0.997604	3.266758e+33	2.546323e+33	0.030388	1.138750e+34
MSPE%	5.439624e+73	0.178050	1.087925e+75	3.127449e+66	1.740829	6.254897e+67	6.044027e+73	0.004408	1.208805e+75
RMSPE%	7.375381e+36	0.421960	3.298371e+37	1.768459e+33	1.319405	7.908791e+33	7.774334e+36	0.066394	3.476788e+37
MAPE	3.693160e+32	0.103256	7.386319e+33	1.056481e+32	0.797643	2.112962e+33	3.986124e+32	0.026101	7.972248e+33
MAPE%	7.375381e+39	0.486633	1.475076e+41	6.438245e+32	3.352859	1.287649e+34	8.194868e+39	0.168163	1.638974e+41

Nobidirectional-FRK-s4_dropout0.0-4000

The experiment has not yet been completed.

Nobidirectional-NoFRK-s4_dropout0.0-4000

The experiment has not yet been completed.

Snapshot

References

Zhu X, Xiong Y, Wu M, et al. Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather Stations[C]//International Conference on Artificial Intelligence and Statistics. PMLR, 2023: 2704-2722.
Juan Lopez Alcaraz, Nils Strodthoff (2022). Diffusion-based time series imputation and forecasting with structured state space models. Transactions on Machine Learning Research. Retrieved from https://openreview.net/forum?id=hHiIbk7ApW
SSSD (2022). GitHub. Retrieved from https://github.com/AI4HealthUOL/SSSD