Hugging Face's logo

Datasets:
Real-TSF
/
TIME-Output

Tasks:
Time Series Forecasting
Modalities:
Tabular
Text
Formats:
csv
Size:
1K - 10K
Tags:
time-series
forecasting
benchmark
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
xet
 Community
25
chris-lettieri-dd commited on
Commit
fdd6df0
·
verified ·
1 Parent(s): 25e9cf5

Submit evaluation results for Toto-2.0-22m

Browse files
This view is limited to 50 files because it contains too many changes.   See raw diff
Files changed (50) hide show
  1. results/Toto-2.0-22m/Australia_Solar/H/long/config.json +40 -0
  2. results/Toto-2.0-22m/Australia_Solar/H/long/metrics.npz +3 -0
  3. results/Toto-2.0-22m/Australia_Solar/H/long/predictions.npz +3 -0
  4. results/Toto-2.0-22m/Australia_Solar/H/medium/config.json +40 -0
  5. results/Toto-2.0-22m/Australia_Solar/H/medium/metrics.npz +3 -0
  6. results/Toto-2.0-22m/Australia_Solar/H/medium/predictions.npz +3 -0
  7. results/Toto-2.0-22m/Australia_Solar/H/short/config.json +40 -0
  8. results/Toto-2.0-22m/Australia_Solar/H/short/metrics.npz +3 -0
  9. results/Toto-2.0-22m/Australia_Solar/H/short/predictions.npz +3 -0
  10. results/Toto-2.0-22m/Auto_Production_SF/M/short/config.json +40 -0
  11. results/Toto-2.0-22m/Auto_Production_SF/M/short/metrics.npz +3 -0
  12. results/Toto-2.0-22m/Auto_Production_SF/M/short/predictions.npz +3 -0
  13. results/Toto-2.0-22m/CPHL/15T/long/config.json +40 -0
  14. results/Toto-2.0-22m/CPHL/15T/long/metrics.npz +3 -0
  15. results/Toto-2.0-22m/CPHL/15T/long/predictions.npz +3 -0
  16. results/Toto-2.0-22m/CPHL/15T/medium/config.json +40 -0
  17. results/Toto-2.0-22m/CPHL/15T/medium/metrics.npz +3 -0
  18. results/Toto-2.0-22m/CPHL/15T/medium/predictions.npz +3 -0
  19. results/Toto-2.0-22m/CPHL/15T/short/config.json +40 -0
  20. results/Toto-2.0-22m/CPHL/15T/short/metrics.npz +3 -0
  21. results/Toto-2.0-22m/CPHL/15T/short/predictions.npz +3 -0
  22. results/Toto-2.0-22m/CPHL/30T/long/config.json +40 -0
  23. results/Toto-2.0-22m/CPHL/30T/long/metrics.npz +3 -0
  24. results/Toto-2.0-22m/CPHL/30T/long/predictions.npz +3 -0
  25. results/Toto-2.0-22m/CPHL/30T/medium/config.json +40 -0
  26. results/Toto-2.0-22m/CPHL/30T/medium/metrics.npz +3 -0
  27. results/Toto-2.0-22m/CPHL/30T/medium/predictions.npz +3 -0
  28. results/Toto-2.0-22m/CPHL/30T/short/config.json +40 -0
  29. results/Toto-2.0-22m/CPHL/30T/short/metrics.npz +3 -0
  30. results/Toto-2.0-22m/CPHL/30T/short/predictions.npz +3 -0
  31. results/Toto-2.0-22m/CPHL/H/long/config.json +40 -0
  32. results/Toto-2.0-22m/CPHL/H/long/metrics.npz +3 -0
  33. results/Toto-2.0-22m/CPHL/H/long/predictions.npz +3 -0
  34. results/Toto-2.0-22m/CPHL/H/medium/config.json +40 -0
  35. results/Toto-2.0-22m/CPHL/H/medium/metrics.npz +3 -0
  36. results/Toto-2.0-22m/CPHL/H/medium/predictions.npz +3 -0
  37. results/Toto-2.0-22m/CPHL/H/short/config.json +40 -0
  38. results/Toto-2.0-22m/CPHL/H/short/metrics.npz +3 -0
  39. results/Toto-2.0-22m/CPHL/H/short/predictions.npz +3 -0
  40. results/Toto-2.0-22m/Coastal_T_S/15T/long/config.json +40 -0
  41. results/Toto-2.0-22m/Coastal_T_S/15T/long/metrics.npz +3 -0
  42. results/Toto-2.0-22m/Coastal_T_S/15T/long/predictions.npz +3 -0
  43. results/Toto-2.0-22m/Coastal_T_S/15T/medium/config.json +40 -0
  44. results/Toto-2.0-22m/Coastal_T_S/15T/medium/metrics.npz +3 -0
  45. results/Toto-2.0-22m/Coastal_T_S/15T/medium/predictions.npz +3 -0
  46. results/Toto-2.0-22m/Coastal_T_S/15T/short/config.json +40 -0
  47. results/Toto-2.0-22m/Coastal_T_S/15T/short/metrics.npz +3 -0
  48. results/Toto-2.0-22m/Coastal_T_S/15T/short/predictions.npz +3 -0
  49. results/Toto-2.0-22m/Coastal_T_S/20T/long/config.json +40 -0
  50. results/Toto-2.0-22m/Coastal_T_S/20T/long/metrics.npz +3 -0
results/Toto-2.0-22m/Australia_Solar/H/long/config.json ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "dataset_config": "Australia_Solar/H/long",
3
+ "num_series": 1,
4
+ "num_windows": 15,
5
+ "num_variates": 3,
6
+ "prediction_length": 168,
7
+ "num_quantiles": 9,
8
+ "quantile_levels": [
9
+ 0.1,
10
+ 0.2,
11
+ 0.3,
12
+ 0.4,
13
+ 0.5,
14
+ 0.6,
15
+ 0.7,
16
+ 0.8,
17
+ 0.9
18
+ ],
19
+ "freq": "H",
20
+ "seasonality": 24,
21
+ "context_length": 4096,
22
+ "metric_names": [
23
+ "MSE",
24
+ "MAE",
25
+ "RMSE",
26
+ "MAPE",
27
+ "sMAPE",
28
+ "MASE",
29
+ "ND",
30
+ "CRPS"
31
+ ],
32
+ "prediction_scale_factor": 100.0,
33
+ "model_name": "Toto-2.0-22m",
34
+ "model_weights_path": "s3://dd-data-science-us1-prod/ray/foundation-models/emaad/20260403_mup_pretrained_toto_v2_final_scaleup/small_pretrained_ps32-e309c867/checkpoint_2026-04-08_23-45-55.670841",
35
+ "batch_size": 512,
36
+ "decode_block_size": 0,
37
+ "imputation_internal": "ffill",
38
+ "scaler_fallback_min_obs": 8,
39
+ "quantile_real_cap_k": 10000.0
40
+ }
results/Toto-2.0-22m/Australia_Solar/H/long/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d76ec07c343f4577770565cc16a71b2fc78bb8ad3dc3c765e5048a300fa4ac39
3
+ size 4540
results/Toto-2.0-22m/Australia_Solar/H/long/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:9c9bc0737c7c051fad5f6449f9d933f6ad4dd3ccb14cc84b2129ffb1a877c84f
3
+ size 123141
results/Toto-2.0-22m/Australia_Solar/H/medium/config.json ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "dataset_config": "Australia_Solar/H/medium",
3
+ "num_series": 1,
4
+ "num_windows": 35,
5
+ "num_variates": 3,
6
+ "prediction_length": 72,
7
+ "num_quantiles": 9,
8
+ "quantile_levels": [
9
+ 0.1,
10
+ 0.2,
11
+ 0.3,
12
+ 0.4,
13
+ 0.5,
14
+ 0.6,
15
+ 0.7,
16
+ 0.8,
17
+ 0.9
18
+ ],
19
+ "freq": "H",
20
+ "seasonality": 24,
21
+ "context_length": 4096,
22
+ "metric_names": [
23
+ "MSE",
24
+ "MAE",
25
+ "RMSE",
26
+ "MAPE",
27
+ "sMAPE",
28
+ "MASE",
29
+ "ND",
30
+ "CRPS"
31
+ ],
32
+ "prediction_scale_factor": 100.0,
33
+ "model_name": "Toto-2.0-22m",
34
+ "model_weights_path": "s3://dd-data-science-us1-prod/ray/foundation-models/emaad/20260403_mup_pretrained_toto_v2_final_scaleup/small_pretrained_ps32-e309c867/checkpoint_2026-04-08_23-45-55.670841",
35
+ "batch_size": 512,
36
+ "decode_block_size": 0,
37
+ "imputation_internal": "ffill",
38
+ "scaler_fallback_min_obs": 8,
39
+ "quantile_real_cap_k": 10000.0
40
+ }
results/Toto-2.0-22m/Australia_Solar/H/medium/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:560b39b0daf9e97733afe3c83a3b14df40417667b538fe0873563eee5578dbd1
3
+ size 8367
results/Toto-2.0-22m/Australia_Solar/H/medium/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d6f5b21c4f41098a0dfdc925cf7853c3117ec9898686321a709db5949b408ac3
3
+ size 123102
results/Toto-2.0-22m/Australia_Solar/H/short/config.json ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "dataset_config": "Australia_Solar/H/short",
3
+ "num_series": 1,
4
+ "num_windows": 105,
5
+ "num_variates": 3,
6
+ "prediction_length": 24,
7
+ "num_quantiles": 9,
8
+ "quantile_levels": [
9
+ 0.1,
10
+ 0.2,
11
+ 0.3,
12
+ 0.4,
13
+ 0.5,
14
+ 0.6,
15
+ 0.7,
16
+ 0.8,
17
+ 0.9
18
+ ],
19
+ "freq": "H",
20
+ "seasonality": 24,
21
+ "context_length": 4096,
22
+ "metric_names": [
23
+ "MSE",
24
+ "MAE",
25
+ "RMSE",
26
+ "MAPE",
27
+ "sMAPE",
28
+ "MASE",
29
+ "ND",
30
+ "CRPS"
31
+ ],
32
+ "prediction_scale_factor": 100.0,
33
+ "model_name": "Toto-2.0-22m",
34
+ "model_weights_path": "s3://dd-data-science-us1-prod/ray/foundation-models/emaad/20260403_mup_pretrained_toto_v2_final_scaleup/small_pretrained_ps32-e309c867/checkpoint_2026-04-08_23-45-55.670841",
35
+ "batch_size": 512,
36
+ "decode_block_size": 0,
37
+ "imputation_internal": "ffill",
38
+ "scaler_fallback_min_obs": 8,
39
+ "quantile_real_cap_k": 10000.0
40
+ }
results/Toto-2.0-22m/Australia_Solar/H/short/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:b53fa392d1c89113d65aa023ece5ac3194489e6aeb127e09052815a02f4bfccb
3
+ size 20768
results/Toto-2.0-22m/Australia_Solar/H/short/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:a8cb7f9509048de67ba32ff9453eb2b2fecd1400c76779e34146c1eaa9b3a93d
3
+ size 122851
results/Toto-2.0-22m/Auto_Production_SF/M/short/config.json ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "dataset_config": "Auto_Production_SF/M/short",
3
+ "num_series": 1,
4
+ "num_windows": 5,
5
+ "num_variates": 1,
6
+ "prediction_length": 12,
7
+ "num_quantiles": 9,
8
+ "quantile_levels": [
9
+ 0.1,
10
+ 0.2,
11
+ 0.3,
12
+ 0.4,
13
+ 0.5,
14
+ 0.6,
15
+ 0.7,
16
+ 0.8,
17
+ 0.9
18
+ ],
19
+ "freq": "M",
20
+ "seasonality": 12,
21
+ "context_length": 4096,
22
+ "metric_names": [
23
+ "MSE",
24
+ "MAE",
25
+ "RMSE",
26
+ "MAPE",
27
+ "sMAPE",
28
+ "MASE",
29
+ "ND",
30
+ "CRPS"
31
+ ],
32
+ "prediction_scale_factor": 1.0,
33
+ "model_name": "Toto-2.0-22m",
34
+ "model_weights_path": "s3://dd-data-science-us1-prod/ray/foundation-models/emaad/20260403_mup_pretrained_toto_v2_final_scaleup/small_pretrained_ps32-e309c867/checkpoint_2026-04-08_23-45-55.670841",
35
+ "batch_size": 512,
36
+ "decode_block_size": 0,
37
+ "imputation_internal": "ffill",
38
+ "scaler_fallback_min_obs": 8,
39
+ "quantile_real_cap_k": 10000.0
40
+ }
results/Toto-2.0-22m/Auto_Production_SF/M/short/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:05693a70159ff35308a9a3fd445f0c186dd1061c98321e66323c4f096460bd13
3
+ size 1823
results/Toto-2.0-22m/Auto_Production_SF/M/short/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:fd761a5635a6cc64aaeb02ae08d6805a68f4aa399c74ee79b8e52bb823bdeef8
3
+ size 1319
results/Toto-2.0-22m/CPHL/15T/long/config.json ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "dataset_config": "CPHL/15T/long",
3
+ "num_series": 2,
4
+ "num_windows": 5,
5
+ "num_variates": 1,
6
+ "prediction_length": 288,
7
+ "num_quantiles": 9,
8
+ "quantile_levels": [
9
+ 0.1,
10
+ 0.2,
11
+ 0.3,
12
+ 0.4,
13
+ 0.5,
14
+ 0.6,
15
+ 0.7,
16
+ 0.8,
17
+ 0.9
18
+ ],
19
+ "freq": "15T",
20
+ "seasonality": 96,
21
+ "context_length": 4096,
22
+ "metric_names": [
23
+ "MSE",
24
+ "MAE",
25
+ "RMSE",
26
+ "MAPE",
27
+ "sMAPE",
28
+ "MASE",
29
+ "ND",
30
+ "CRPS"
31
+ ],
32
+ "prediction_scale_factor": 1.0,
33
+ "model_name": "Toto-2.0-22m",
34
+ "model_weights_path": "s3://dd-data-science-us1-prod/ray/foundation-models/emaad/20260403_mup_pretrained_toto_v2_final_scaleup/small_pretrained_ps32-e309c867/checkpoint_2026-04-08_23-45-55.670841",
35
+ "batch_size": 512,
36
+ "decode_block_size": 0,
37
+ "imputation_internal": "ffill",
38
+ "scaler_fallback_min_obs": 8,
39
+ "quantile_real_cap_k": 10000.0
40
+ }
results/Toto-2.0-22m/CPHL/15T/long/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:5b729383a2e35bed56e5a1e96b5c2706a4d600d5cb4e5ef05dd63668573e957b
3
+ size 2171
results/Toto-2.0-22m/CPHL/15T/long/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:686b63cb02689a86cfa8d2aea4886a0a9723bf454bdab70c1dadc862a3d19efe
3
+ size 38029
results/Toto-2.0-22m/CPHL/15T/medium/config.json ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "dataset_config": "CPHL/15T/medium",
3
+ "num_series": 2,
4
+ "num_windows": 15,
5
+ "num_variates": 1,
6
+ "prediction_length": 96,
7
+ "num_quantiles": 9,
8
+ "quantile_levels": [
9
+ 0.1,
10
+ 0.2,
11
+ 0.3,
12
+ 0.4,
13
+ 0.5,
14
+ 0.6,
15
+ 0.7,
16
+ 0.8,
17
+ 0.9
18
+ ],
19
+ "freq": "15T",
20
+ "seasonality": 96,
21
+ "context_length": 4096,
22
+ "metric_names": [
23
+ "MSE",
24
+ "MAE",
25
+ "RMSE",
26
+ "MAPE",
27
+ "sMAPE",
28
+ "MASE",
29
+ "ND",
30
+ "CRPS"
31
+ ],
32
+ "prediction_scale_factor": 1.0,
33
+ "model_name": "Toto-2.0-22m",
34
+ "model_weights_path": "s3://dd-data-science-us1-prod/ray/foundation-models/emaad/20260403_mup_pretrained_toto_v2_final_scaleup/small_pretrained_ps32-e309c867/checkpoint_2026-04-08_23-45-55.670841",
35
+ "batch_size": 512,
36
+ "decode_block_size": 0,
37
+ "imputation_internal": "ffill",
38
+ "scaler_fallback_min_obs": 8,
39
+ "quantile_real_cap_k": 10000.0
40
+ }
results/Toto-2.0-22m/CPHL/15T/medium/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:db181393495ec212f91d476707afccf3a6f7ebdb1c6d5f6b8ba82ba019917410
3
+ size 3470
results/Toto-2.0-22m/CPHL/15T/medium/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:e93a9abd66796a6c6e40b3754f06d4595776d03b832bed0b04c431015bdcb341
3
+ size 38823
results/Toto-2.0-22m/CPHL/15T/short/config.json ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "dataset_config": "CPHL/15T/short",
3
+ "num_series": 2,
4
+ "num_windows": 120,
5
+ "num_variates": 1,
6
+ "prediction_length": 12,
7
+ "num_quantiles": 9,
8
+ "quantile_levels": [
9
+ 0.1,
10
+ 0.2,
11
+ 0.3,
12
+ 0.4,
13
+ 0.5,
14
+ 0.6,
15
+ 0.7,
16
+ 0.8,
17
+ 0.9
18
+ ],
19
+ "freq": "15T",
20
+ "seasonality": 96,
21
+ "context_length": 4096,
22
+ "metric_names": [
23
+ "MSE",
24
+ "MAE",
25
+ "RMSE",
26
+ "MAPE",
27
+ "sMAPE",
28
+ "MASE",
29
+ "ND",
30
+ "CRPS"
31
+ ],
32
+ "prediction_scale_factor": 1.0,
33
+ "model_name": "Toto-2.0-22m",
34
+ "model_weights_path": "s3://dd-data-science-us1-prod/ray/foundation-models/emaad/20260403_mup_pretrained_toto_v2_final_scaleup/small_pretrained_ps32-e309c867/checkpoint_2026-04-08_23-45-55.670841",
35
+ "batch_size": 512,
36
+ "decode_block_size": 0,
37
+ "imputation_internal": "ffill",
38
+ "scaler_fallback_min_obs": 8,
39
+ "quantile_real_cap_k": 10000.0
40
+ }
results/Toto-2.0-22m/CPHL/15T/short/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:8bfc584f922706657fde90f3b6e313f712a460763e7820781056f91a6c2d6999
3
+ size 15760
results/Toto-2.0-22m/CPHL/15T/short/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:1ae8c2b93389468cce7e3f4c169ef9374eb1c3648a2b8e312efcec6ecee40cf7
3
+ size 40178
results/Toto-2.0-22m/CPHL/30T/long/config.json ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "dataset_config": "CPHL/30T/long",
3
+ "num_series": 2,
4
+ "num_windows": 10,
5
+ "num_variates": 1,
6
+ "prediction_length": 144,
7
+ "num_quantiles": 9,
8
+ "quantile_levels": [
9
+ 0.1,
10
+ 0.2,
11
+ 0.3,
12
+ 0.4,
13
+ 0.5,
14
+ 0.6,
15
+ 0.7,
16
+ 0.8,
17
+ 0.9
18
+ ],
19
+ "freq": "30T",
20
+ "seasonality": 48,
21
+ "context_length": 4096,
22
+ "metric_names": [
23
+ "MSE",
24
+ "MAE",
25
+ "RMSE",
26
+ "MAPE",
27
+ "sMAPE",
28
+ "MASE",
29
+ "ND",
30
+ "CRPS"
31
+ ],
32
+ "prediction_scale_factor": 1.0,
33
+ "model_name": "Toto-2.0-22m",
34
+ "model_weights_path": "s3://dd-data-science-us1-prod/ray/foundation-models/emaad/20260403_mup_pretrained_toto_v2_final_scaleup/small_pretrained_ps32-e309c867/checkpoint_2026-04-08_23-45-55.670841",
35
+ "batch_size": 512,
36
+ "decode_block_size": 0,
37
+ "imputation_internal": "ffill",
38
+ "scaler_fallback_min_obs": 8,
39
+ "quantile_real_cap_k": 10000.0
40
+ }
results/Toto-2.0-22m/CPHL/30T/long/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:372207824ce75eef7e82c37dee34e7a9d3eddab51aea4afde453079df098b58e
3
+ size 2841
results/Toto-2.0-22m/CPHL/30T/long/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:e464aaa27f0368d3ba1cac26bbef6fdf432b8a5d34fb83943fbd3f57441c21db
3
+ size 40612
results/Toto-2.0-22m/CPHL/30T/medium/config.json ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "dataset_config": "CPHL/30T/medium",
3
+ "num_series": 2,
4
+ "num_windows": 30,
5
+ "num_variates": 1,
6
+ "prediction_length": 48,
7
+ "num_quantiles": 9,
8
+ "quantile_levels": [
9
+ 0.1,
10
+ 0.2,
11
+ 0.3,
12
+ 0.4,
13
+ 0.5,
14
+ 0.6,
15
+ 0.7,
16
+ 0.8,
17
+ 0.9
18
+ ],
19
+ "freq": "30T",
20
+ "seasonality": 48,
21
+ "context_length": 4096,
22
+ "metric_names": [
23
+ "MSE",
24
+ "MAE",
25
+ "RMSE",
26
+ "MAPE",
27
+ "sMAPE",
28
+ "MASE",
29
+ "ND",
30
+ "CRPS"
31
+ ],
32
+ "prediction_scale_factor": 1.0,
33
+ "model_name": "Toto-2.0-22m",
34
+ "model_weights_path": "s3://dd-data-science-us1-prod/ray/foundation-models/emaad/20260403_mup_pretrained_toto_v2_final_scaleup/small_pretrained_ps32-e309c867/checkpoint_2026-04-08_23-45-55.670841",
35
+ "batch_size": 512,
36
+ "decode_block_size": 0,
37
+ "imputation_internal": "ffill",
38
+ "scaler_fallback_min_obs": 8,
39
+ "quantile_real_cap_k": 10000.0
40
+ }
results/Toto-2.0-22m/CPHL/30T/medium/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:6d516245b26a15b946bfdcb1e96f989fd63d1d6339d4c269643b2409f8cf645b
3
+ size 5463
results/Toto-2.0-22m/CPHL/30T/medium/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:8f34f8e6cc3b32760f1a226a9372e111e7c8ea83e143e1f123ae2dfaf0593a19
3
+ size 40872
results/Toto-2.0-22m/CPHL/30T/short/config.json ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "dataset_config": "CPHL/30T/short",
3
+ "num_series": 2,
4
+ "num_windows": 120,
5
+ "num_variates": 1,
6
+ "prediction_length": 12,
7
+ "num_quantiles": 9,
8
+ "quantile_levels": [
9
+ 0.1,
10
+ 0.2,
11
+ 0.3,
12
+ 0.4,
13
+ 0.5,
14
+ 0.6,
15
+ 0.7,
16
+ 0.8,
17
+ 0.9
18
+ ],
19
+ "freq": "30T",
20
+ "seasonality": 48,
21
+ "context_length": 4096,
22
+ "metric_names": [
23
+ "MSE",
24
+ "MAE",
25
+ "RMSE",
26
+ "MAPE",
27
+ "sMAPE",
28
+ "MASE",
29
+ "ND",
30
+ "CRPS"
31
+ ],
32
+ "prediction_scale_factor": 1.0,
33
+ "model_name": "Toto-2.0-22m",
34
+ "model_weights_path": "s3://dd-data-science-us1-prod/ray/foundation-models/emaad/20260403_mup_pretrained_toto_v2_final_scaleup/small_pretrained_ps32-e309c867/checkpoint_2026-04-08_23-45-55.670841",
35
+ "batch_size": 512,
36
+ "decode_block_size": 0,
37
+ "imputation_internal": "ffill",
38
+ "scaler_fallback_min_obs": 8,
39
+ "quantile_real_cap_k": 10000.0
40
+ }
results/Toto-2.0-22m/CPHL/30T/short/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:ceeb2fd0b19ddddf7da5a2e99844d75f9e3c4524dc09b03d66e498e6ef224a52
3
+ size 15879
results/Toto-2.0-22m/CPHL/30T/short/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:42179e0843873df195625d0963bf02bcf53e91dd2f73d4a76bbbd85dfaacc56d
3
+ size 41510
results/Toto-2.0-22m/CPHL/H/long/config.json ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "dataset_config": "CPHL/H/long",
3
+ "num_series": 4,
4
+ "num_windows": 2,
5
+ "num_variates": 1,
6
+ "prediction_length": 336,
7
+ "num_quantiles": 9,
8
+ "quantile_levels": [
9
+ 0.1,
10
+ 0.2,
11
+ 0.3,
12
+ 0.4,
13
+ 0.5,
14
+ 0.6,
15
+ 0.7,
16
+ 0.8,
17
+ 0.9
18
+ ],
19
+ "freq": "H",
20
+ "seasonality": 24,
21
+ "context_length": 4096,
22
+ "metric_names": [
23
+ "MSE",
24
+ "MAE",
25
+ "RMSE",
26
+ "MAPE",
27
+ "sMAPE",
28
+ "MASE",
29
+ "ND",
30
+ "CRPS"
31
+ ],
32
+ "prediction_scale_factor": 1.0,
33
+ "model_name": "Toto-2.0-22m",
34
+ "model_weights_path": "s3://dd-data-science-us1-prod/ray/foundation-models/emaad/20260403_mup_pretrained_toto_v2_final_scaleup/small_pretrained_ps32-e309c867/checkpoint_2026-04-08_23-45-55.670841",
35
+ "batch_size": 512,
36
+ "decode_block_size": 0,
37
+ "imputation_internal": "ffill",
38
+ "scaler_fallback_min_obs": 8,
39
+ "quantile_real_cap_k": 10000.0
40
+ }
results/Toto-2.0-22m/CPHL/H/long/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3ce9b599a946e1436ac0c7812b6b3d93b3d1446de75aec1a8b844b09d69de198
3
+ size 2038
results/Toto-2.0-22m/CPHL/H/long/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:9d6cb7532b0b4777768063e17bbc8e9e2404d2a694a1dddb6a84e9820423b9a5
3
+ size 37683
results/Toto-2.0-22m/CPHL/H/medium/config.json ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "dataset_config": "CPHL/H/medium",
3
+ "num_series": 4,
4
+ "num_windows": 4,
5
+ "num_variates": 1,
6
+ "prediction_length": 168,
7
+ "num_quantiles": 9,
8
+ "quantile_levels": [
9
+ 0.1,
10
+ 0.2,
11
+ 0.3,
12
+ 0.4,
13
+ 0.5,
14
+ 0.6,
15
+ 0.7,
16
+ 0.8,
17
+ 0.9
18
+ ],
19
+ "freq": "H",
20
+ "seasonality": 24,
21
+ "context_length": 4096,
22
+ "metric_names": [
23
+ "MSE",
24
+ "MAE",
25
+ "RMSE",
26
+ "MAPE",
27
+ "sMAPE",
28
+ "MASE",
29
+ "ND",
30
+ "CRPS"
31
+ ],
32
+ "prediction_scale_factor": 1.0,
33
+ "model_name": "Toto-2.0-22m",
34
+ "model_weights_path": "s3://dd-data-science-us1-prod/ray/foundation-models/emaad/20260403_mup_pretrained_toto_v2_final_scaleup/small_pretrained_ps32-e309c867/checkpoint_2026-04-08_23-45-55.670841",
35
+ "batch_size": 512,
36
+ "decode_block_size": 0,
37
+ "imputation_internal": "ffill",
38
+ "scaler_fallback_min_obs": 8,
39
+ "quantile_real_cap_k": 10000.0
40
+ }
results/Toto-2.0-22m/CPHL/H/medium/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3f68a0463485b5827f9ddc29e6c842c1f9f39108958db260f08a99cba29a3383
3
+ size 2566
results/Toto-2.0-22m/CPHL/H/medium/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f96d9a2ed8686f982839c5b015f466f197e3f582e33c533d917c2c9e79b5fad0
3
+ size 38285
results/Toto-2.0-22m/CPHL/H/short/config.json ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "dataset_config": "CPHL/H/short",
3
+ "num_series": 4,
4
+ "num_windows": 28,
5
+ "num_variates": 1,
6
+ "prediction_length": 24,
7
+ "num_quantiles": 9,
8
+ "quantile_levels": [
9
+ 0.1,
10
+ 0.2,
11
+ 0.3,
12
+ 0.4,
13
+ 0.5,
14
+ 0.6,
15
+ 0.7,
16
+ 0.8,
17
+ 0.9
18
+ ],
19
+ "freq": "H",
20
+ "seasonality": 24,
21
+ "context_length": 4096,
22
+ "metric_names": [
23
+ "MSE",
24
+ "MAE",
25
+ "RMSE",
26
+ "MAPE",
27
+ "sMAPE",
28
+ "MASE",
29
+ "ND",
30
+ "CRPS"
31
+ ],
32
+ "prediction_scale_factor": 1.0,
33
+ "model_name": "Toto-2.0-22m",
34
+ "model_weights_path": "s3://dd-data-science-us1-prod/ray/foundation-models/emaad/20260403_mup_pretrained_toto_v2_final_scaleup/small_pretrained_ps32-e309c867/checkpoint_2026-04-08_23-45-55.670841",
35
+ "batch_size": 512,
36
+ "decode_block_size": 0,
37
+ "imputation_internal": "ffill",
38
+ "scaler_fallback_min_obs": 8,
39
+ "quantile_real_cap_k": 10000.0
40
+ }
results/Toto-2.0-22m/CPHL/H/short/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:6e770158b242d7ee754fee7c178670ef4533cedeb168326e333577d6bd8a2c84
3
+ size 8497
results/Toto-2.0-22m/CPHL/H/short/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:07838a4d0c9a5677c266ea4567ec932088cf0135c281750120287e0d9c8bc570
3
+ size 39218
results/Toto-2.0-22m/Coastal_T_S/15T/long/config.json ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "dataset_config": "Coastal_T_S/15T/long",
3
+ "num_series": 5,
4
+ "num_windows": 5,
5
+ "num_variates": 3,
6
+ "prediction_length": 288,
7
+ "num_quantiles": 9,
8
+ "quantile_levels": [
9
+ 0.1,
10
+ 0.2,
11
+ 0.3,
12
+ 0.4,
13
+ 0.5,
14
+ 0.6,
15
+ 0.7,
16
+ 0.8,
17
+ 0.9
18
+ ],
19
+ "freq": "15T",
20
+ "seasonality": 96,
21
+ "context_length": 4096,
22
+ "metric_names": [
23
+ "MSE",
24
+ "MAE",
25
+ "RMSE",
26
+ "MAPE",
27
+ "sMAPE",
28
+ "MASE",
29
+ "ND",
30
+ "CRPS"
31
+ ],
32
+ "prediction_scale_factor": 1.0,
33
+ "model_name": "Toto-2.0-22m",
34
+ "model_weights_path": "s3://dd-data-science-us1-prod/ray/foundation-models/emaad/20260403_mup_pretrained_toto_v2_final_scaleup/small_pretrained_ps32-e309c867/checkpoint_2026-04-08_23-45-55.670841",
35
+ "batch_size": 512,
36
+ "decode_block_size": 0,
37
+ "imputation_internal": "ffill",
38
+ "scaler_fallback_min_obs": 8,
39
+ "quantile_real_cap_k": 10000.0
40
+ }
results/Toto-2.0-22m/Coastal_T_S/15T/long/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:522b6e8eef3e918bdbb689bb8350c7e834d9946787622acdeec14f6853cd192c
3
+ size 6281
results/Toto-2.0-22m/Coastal_T_S/15T/long/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:4d9dcda42347f60e71cde78210b822b50904ca115b26eb4b487343917c049ce8
3
+ size 90031
results/Toto-2.0-22m/Coastal_T_S/15T/medium/config.json ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "dataset_config": "Coastal_T_S/15T/medium",
3
+ "num_series": 5,
4
+ "num_windows": 15,
5
+ "num_variates": 3,
6
+ "prediction_length": 96,
7
+ "num_quantiles": 9,
8
+ "quantile_levels": [
9
+ 0.1,
10
+ 0.2,
11
+ 0.3,
12
+ 0.4,
13
+ 0.5,
14
+ 0.6,
15
+ 0.7,
16
+ 0.8,
17
+ 0.9
18
+ ],
19
+ "freq": "15T",
20
+ "seasonality": 96,
21
+ "context_length": 4096,
22
+ "metric_names": [
23
+ "MSE",
24
+ "MAE",
25
+ "RMSE",
26
+ "MAPE",
27
+ "sMAPE",
28
+ "MASE",
29
+ "ND",
30
+ "CRPS"
31
+ ],
32
+ "prediction_scale_factor": 1.0,
33
+ "model_name": "Toto-2.0-22m",
34
+ "model_weights_path": "s3://dd-data-science-us1-prod/ray/foundation-models/emaad/20260403_mup_pretrained_toto_v2_final_scaleup/small_pretrained_ps32-e309c867/checkpoint_2026-04-08_23-45-55.670841",
35
+ "batch_size": 512,
36
+ "decode_block_size": 0,
37
+ "imputation_internal": "ffill",
38
+ "scaler_fallback_min_obs": 8,
39
+ "quantile_real_cap_k": 10000.0
40
+ }
results/Toto-2.0-22m/Coastal_T_S/15T/medium/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:88ead855711979947585379ee21f57f31be7e0c4b19a4c0ff02b50f322961cfe
3
+ size 14848
results/Toto-2.0-22m/Coastal_T_S/15T/medium/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:55e43811b29d06f24f7085533e8ea5c1dccf231e794a00f9a40ea5660efe9091
3
+ size 97276
results/Toto-2.0-22m/Coastal_T_S/15T/short/config.json ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "dataset_config": "Coastal_T_S/15T/short",
3
+ "num_series": 5,
4
+ "num_windows": 120,
5
+ "num_variates": 3,
6
+ "prediction_length": 12,
7
+ "num_quantiles": 9,
8
+ "quantile_levels": [
9
+ 0.1,
10
+ 0.2,
11
+ 0.3,
12
+ 0.4,
13
+ 0.5,
14
+ 0.6,
15
+ 0.7,
16
+ 0.8,
17
+ 0.9
18
+ ],
19
+ "freq": "15T",
20
+ "seasonality": 96,
21
+ "context_length": 4096,
22
+ "metric_names": [
23
+ "MSE",
24
+ "MAE",
25
+ "RMSE",
26
+ "MAPE",
27
+ "sMAPE",
28
+ "MASE",
29
+ "ND",
30
+ "CRPS"
31
+ ],
32
+ "prediction_scale_factor": 1.0,
33
+ "model_name": "Toto-2.0-22m",
34
+ "model_weights_path": "s3://dd-data-science-us1-prod/ray/foundation-models/emaad/20260403_mup_pretrained_toto_v2_final_scaleup/small_pretrained_ps32-e309c867/checkpoint_2026-04-08_23-45-55.670841",
35
+ "batch_size": 512,
36
+ "decode_block_size": 0,
37
+ "imputation_internal": "ffill",
38
+ "scaler_fallback_min_obs": 8,
39
+ "quantile_real_cap_k": 10000.0
40
+ }
results/Toto-2.0-22m/Coastal_T_S/15T/short/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:5927efda534af49d56627f958f6b0efdcdbf8ee165fe76428fffd4d3b0e74655
3
+ size 103052
results/Toto-2.0-22m/Coastal_T_S/15T/short/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:59b7e553d6286d137289948e2110ce454fd178bebadb0dc12aa66f042c29f3df
3
+ size 128819
results/Toto-2.0-22m/Coastal_T_S/20T/long/config.json ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "dataset_config": "Coastal_T_S/20T/long",
3
+ "num_series": 1,
4
+ "num_windows": 5,
5
+ "num_variates": 3,
6
+ "prediction_length": 216,
7
+ "num_quantiles": 9,
8
+ "quantile_levels": [
9
+ 0.1,
10
+ 0.2,
11
+ 0.3,
12
+ 0.4,
13
+ 0.5,
14
+ 0.6,
15
+ 0.7,
16
+ 0.8,
17
+ 0.9
18
+ ],
19
+ "freq": "20T",
20
+ "seasonality": 72,
21
+ "context_length": 4096,
22
+ "metric_names": [
23
+ "MSE",
24
+ "MAE",
25
+ "RMSE",
26
+ "MAPE",
27
+ "sMAPE",
28
+ "MASE",
29
+ "ND",
30
+ "CRPS"
31
+ ],
32
+ "prediction_scale_factor": 1.0,
33
+ "model_name": "Toto-2.0-22m",
34
+ "model_weights_path": "s3://dd-data-science-us1-prod/ray/foundation-models/emaad/20260403_mup_pretrained_toto_v2_final_scaleup/small_pretrained_ps32-e309c867/checkpoint_2026-04-08_23-45-55.670841",
35
+ "batch_size": 512,
36
+ "decode_block_size": 0,
37
+ "imputation_internal": "ffill",
38
+ "scaler_fallback_min_obs": 8,
39
+ "quantile_real_cap_k": 10000.0
40
+ }
results/Toto-2.0-22m/Coastal_T_S/20T/long/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f5954a414935f7df1db3fbde79edd2eee75574c95c7264747500c1ca83f35456
3
+ size 2487