Submit evaluation results for Timestral-123M

#53
This view is limited to 50 files because it contains too many changes. See the raw diff here.
Files changed (50) hide show
  1. results/Timestral-123M/Australia_Solar/H/long/config.json +40 -0
  2. results/Timestral-123M/Australia_Solar/H/long/metrics.npz +3 -0
  3. results/Timestral-123M/Australia_Solar/H/long/predictions.npz +3 -0
  4. results/Timestral-123M/Australia_Solar/H/medium/config.json +40 -0
  5. results/Timestral-123M/Australia_Solar/H/medium/metrics.npz +3 -0
  6. results/Timestral-123M/Australia_Solar/H/medium/predictions.npz +3 -0
  7. results/Timestral-123M/Australia_Solar/H/short/config.json +40 -0
  8. results/Timestral-123M/Australia_Solar/H/short/metrics.npz +3 -0
  9. results/Timestral-123M/Australia_Solar/H/short/predictions.npz +3 -0
  10. results/Timestral-123M/Auto_Production_SF/M/short/config.json +40 -0
  11. results/Timestral-123M/Auto_Production_SF/M/short/metrics.npz +3 -0
  12. results/Timestral-123M/Auto_Production_SF/M/short/predictions.npz +3 -0
  13. results/Timestral-123M/CPHL/15T/long/config.json +40 -0
  14. results/Timestral-123M/CPHL/15T/long/metrics.npz +3 -0
  15. results/Timestral-123M/CPHL/15T/long/predictions.npz +3 -0
  16. results/Timestral-123M/CPHL/15T/medium/config.json +40 -0
  17. results/Timestral-123M/CPHL/15T/medium/metrics.npz +3 -0
  18. results/Timestral-123M/CPHL/15T/medium/predictions.npz +3 -0
  19. results/Timestral-123M/CPHL/15T/short/config.json +40 -0
  20. results/Timestral-123M/CPHL/15T/short/metrics.npz +3 -0
  21. results/Timestral-123M/CPHL/15T/short/predictions.npz +3 -0
  22. results/Timestral-123M/CPHL/30T/long/config.json +40 -0
  23. results/Timestral-123M/CPHL/30T/long/metrics.npz +3 -0
  24. results/Timestral-123M/CPHL/30T/long/predictions.npz +3 -0
  25. results/Timestral-123M/CPHL/30T/medium/config.json +40 -0
  26. results/Timestral-123M/CPHL/30T/medium/metrics.npz +3 -0
  27. results/Timestral-123M/CPHL/30T/medium/predictions.npz +3 -0
  28. results/Timestral-123M/CPHL/30T/short/config.json +40 -0
  29. results/Timestral-123M/CPHL/30T/short/metrics.npz +3 -0
  30. results/Timestral-123M/CPHL/30T/short/predictions.npz +3 -0
  31. results/Timestral-123M/CPHL/H/long/config.json +40 -0
  32. results/Timestral-123M/CPHL/H/long/metrics.npz +3 -0
  33. results/Timestral-123M/CPHL/H/long/predictions.npz +3 -0
  34. results/Timestral-123M/CPHL/H/medium/config.json +40 -0
  35. results/Timestral-123M/CPHL/H/medium/metrics.npz +3 -0
  36. results/Timestral-123M/CPHL/H/medium/predictions.npz +3 -0
  37. results/Timestral-123M/CPHL/H/short/config.json +40 -0
  38. results/Timestral-123M/CPHL/H/short/metrics.npz +3 -0
  39. results/Timestral-123M/CPHL/H/short/predictions.npz +3 -0
  40. results/Timestral-123M/Coastal_T_S/15T/long/config.json +40 -0
  41. results/Timestral-123M/Coastal_T_S/15T/long/metrics.npz +3 -0
  42. results/Timestral-123M/Coastal_T_S/15T/long/predictions.npz +3 -0
  43. results/Timestral-123M/Coastal_T_S/15T/medium/config.json +40 -0
  44. results/Timestral-123M/Coastal_T_S/15T/medium/metrics.npz +3 -0
  45. results/Timestral-123M/Coastal_T_S/15T/medium/predictions.npz +3 -0
  46. results/Timestral-123M/Coastal_T_S/15T/short/config.json +40 -0
  47. results/Timestral-123M/Coastal_T_S/15T/short/metrics.npz +3 -0
  48. results/Timestral-123M/Coastal_T_S/15T/short/predictions.npz +3 -0
  49. results/Timestral-123M/Coastal_T_S/20T/long/config.json +40 -0
  50. results/Timestral-123M/Coastal_T_S/20T/long/metrics.npz +3 -0
results/Timestral-123M/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": 34896,
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": "Timestral-123M",
34
+ "model_id": "mistralai/Timestral-123M",
35
+ "max_context": 32768,
36
+ "per_shot_horizon_patches": 32,
37
+ "symmetric_averaging": true,
38
+ "clip_nonnegative": true,
39
+ "to_univariate": false
40
+ }
results/Timestral-123M/Australia_Solar/H/long/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d49444f64961a0cc2cb64f2e2eb47cd387c08acbc359e1bf3141b56a55d81ded
3
+ size 4537
results/Timestral-123M/Australia_Solar/H/long/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d157e79a714eff3a43070ebb2c02d02714a62eeadbe2045355334bf865f030a9
3
+ size 119312
results/Timestral-123M/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": 34992,
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": "Timestral-123M",
34
+ "model_id": "mistralai/Timestral-123M",
35
+ "max_context": 32768,
36
+ "per_shot_horizon_patches": 32,
37
+ "symmetric_averaging": true,
38
+ "clip_nonnegative": true,
39
+ "to_univariate": false
40
+ }
results/Timestral-123M/Australia_Solar/H/medium/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f1be5aabd8e4821af27e9c56f68d2cf9bd4997107d4bf68a667bf95d5c7ac99c
3
+ size 8362
results/Timestral-123M/Australia_Solar/H/medium/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:7c95cd25d591d4712c1d1dff993d8f5e3b729dd9f3e1236955aca929c3a5c5bd
3
+ size 120305
results/Timestral-123M/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": 35040,
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": "Timestral-123M",
34
+ "model_id": "mistralai/Timestral-123M",
35
+ "max_context": 32768,
36
+ "per_shot_horizon_patches": 32,
37
+ "symmetric_averaging": true,
38
+ "clip_nonnegative": true,
39
+ "to_univariate": false
40
+ }
results/Timestral-123M/Australia_Solar/H/short/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:2543712cd0f0b74874365a8ff2eb4164792b06ee197e522a88b9ca2d5ad0c931
3
+ size 20769
results/Timestral-123M/Australia_Solar/H/short/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:423a65e55be121e2bf84bea16f8d30a3e0710c5f056db25040d50896e4c12304
3
+ size 120259
results/Timestral-123M/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": 355,
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": "Timestral-123M",
34
+ "model_id": "mistralai/Timestral-123M",
35
+ "max_context": 32768,
36
+ "per_shot_horizon_patches": 32,
37
+ "symmetric_averaging": true,
38
+ "clip_nonnegative": true,
39
+ "to_univariate": false
40
+ }
results/Timestral-123M/Auto_Production_SF/M/short/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:e18b9a8044f82dd6933efdc3660e3a2587ef61e651f477b8ef25752cd505a6c7
3
+ size 1825
results/Timestral-123M/Auto_Production_SF/M/short/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:8a812dc817ea68094aca866239db525ae188d11b4a78a5c9085fc07289ecdcbe
3
+ size 1323
results/Timestral-123M/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": 10543,
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": "Timestral-123M",
34
+ "model_id": "mistralai/Timestral-123M",
35
+ "max_context": 32768,
36
+ "per_shot_horizon_patches": 32,
37
+ "symmetric_averaging": true,
38
+ "clip_nonnegative": true,
39
+ "to_univariate": false
40
+ }
results/Timestral-123M/CPHL/15T/long/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:be917403d4b92a29e740915726b098b4fac84b2bd53f7dcb94ce42eabf39bec3
3
+ size 2174
results/Timestral-123M/CPHL/15T/long/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:20525f663f3861ee6ff4e16bc9d16971519c2954db05db7970b1e1d78b80f6cf
3
+ size 38138
results/Timestral-123M/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": 10735,
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": "Timestral-123M",
34
+ "model_id": "mistralai/Timestral-123M",
35
+ "max_context": 32768,
36
+ "per_shot_horizon_patches": 32,
37
+ "symmetric_averaging": true,
38
+ "clip_nonnegative": true,
39
+ "to_univariate": false
40
+ }
results/Timestral-123M/CPHL/15T/medium/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:42f323b510d6e722dbd6f8896a87abf236e235f17a77ab83db7f8099babdc24f
3
+ size 3462
results/Timestral-123M/CPHL/15T/medium/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:6bdce1e92a8f1abb9960da3d19db148c8db5df70c160eddb59d53208b689175d
3
+ size 38865
results/Timestral-123M/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": 10819,
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": "Timestral-123M",
34
+ "model_id": "mistralai/Timestral-123M",
35
+ "max_context": 32768,
36
+ "per_shot_horizon_patches": 32,
37
+ "symmetric_averaging": true,
38
+ "clip_nonnegative": true,
39
+ "to_univariate": false
40
+ }
results/Timestral-123M/CPHL/15T/short/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:e86763ea9c2b1792e671e6d7ec97e3ea1c8290f78d1bb9428a84af6dbc871b87
3
+ size 15746
results/Timestral-123M/CPHL/15T/short/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:09a25eceef64defa35f0662ffdb0a2c95c17918cea4762cc3a6b4d728cb63918
3
+ size 39975
results/Timestral-123M/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": 17422,
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": "Timestral-123M",
34
+ "model_id": "mistralai/Timestral-123M",
35
+ "max_context": 32768,
36
+ "per_shot_horizon_patches": 32,
37
+ "symmetric_averaging": true,
38
+ "clip_nonnegative": true,
39
+ "to_univariate": false
40
+ }
results/Timestral-123M/CPHL/30T/long/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:e470f10920448c597a3235dc142be6182e758ffd3450e85c3863dfd4a03e216f
3
+ size 2839
results/Timestral-123M/CPHL/30T/long/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:c1d1b7319e31148c7db2b3a287f40a410a2e7baebaa64f87c8510082f52e7018
3
+ size 40484
results/Timestral-123M/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": 17518,
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": "Timestral-123M",
34
+ "model_id": "mistralai/Timestral-123M",
35
+ "max_context": 32768,
36
+ "per_shot_horizon_patches": 32,
37
+ "symmetric_averaging": true,
38
+ "clip_nonnegative": true,
39
+ "to_univariate": false
40
+ }
results/Timestral-123M/CPHL/30T/medium/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:5fa8a9fb9d17bbcb70842c8217fa068c6eb1f7c27256f855eb99a1e45bb552e9
3
+ size 5462
results/Timestral-123M/CPHL/30T/medium/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:7badef3e9fd46198fcb5db29e7791742cb632e23791a6df357b7ddfb74591491
3
+ size 40749
results/Timestral-123M/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": 17554,
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": "Timestral-123M",
34
+ "model_id": "mistralai/Timestral-123M",
35
+ "max_context": 32768,
36
+ "per_shot_horizon_patches": 32,
37
+ "symmetric_averaging": true,
38
+ "clip_nonnegative": true,
39
+ "to_univariate": false
40
+ }
results/Timestral-123M/CPHL/30T/short/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d545de672dad38a04372d05ed8f3642077b65538688d1925273dd88497b14a2a
3
+ size 15861
results/Timestral-123M/CPHL/30T/short/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:683d1b72d3fe1a103359ee8b28fc9dcfdc7ab967c6ad2fd4150404b4c32ca121
3
+ size 41358
results/Timestral-123M/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": 8447,
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": "Timestral-123M",
34
+ "model_id": "mistralai/Timestral-123M",
35
+ "max_context": 32768,
36
+ "per_shot_horizon_patches": 32,
37
+ "symmetric_averaging": true,
38
+ "clip_nonnegative": true,
39
+ "to_univariate": false
40
+ }
results/Timestral-123M/CPHL/H/long/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:36848cc39677e0ea3ce68f02daaec419702bce51a4855f22aadd271e1039c1f5
3
+ size 2041
results/Timestral-123M/CPHL/H/long/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:ce14d7314f38a1e39bfb4bd8296a75836ad1b4376364944498a5b4352c6d01b3
3
+ size 37928
results/Timestral-123M/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": 8615,
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": "Timestral-123M",
34
+ "model_id": "mistralai/Timestral-123M",
35
+ "max_context": 32768,
36
+ "per_shot_horizon_patches": 32,
37
+ "symmetric_averaging": true,
38
+ "clip_nonnegative": true,
39
+ "to_univariate": false
40
+ }
results/Timestral-123M/CPHL/H/medium/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:739836d7c86197668ce88392c9262843a8029c0db301e93357956767bf5883fb
3
+ size 2557
results/Timestral-123M/CPHL/H/medium/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:c8fecf185c8234aef0547f0566e744da55d2f300a75f7d2a9a62000244aca3af
3
+ size 38428
results/Timestral-123M/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": 8759,
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": "Timestral-123M",
34
+ "model_id": "mistralai/Timestral-123M",
35
+ "max_context": 32768,
36
+ "per_shot_horizon_patches": 32,
37
+ "symmetric_averaging": true,
38
+ "clip_nonnegative": true,
39
+ "to_univariate": false
40
+ }
results/Timestral-123M/CPHL/H/short/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f33965c836896c9d23d86cf8b62b3fc25d4bf7c04da19c024d970e50f1bb6e95
3
+ size 8505
results/Timestral-123M/CPHL/H/short/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3f3c7bd647e31684ccb3cebc76822fe373ea615b2601edc3c17666f0b10c9aa9
3
+ size 39225
results/Timestral-123M/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": 22846,
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": "Timestral-123M",
34
+ "model_id": "mistralai/Timestral-123M",
35
+ "max_context": 32768,
36
+ "per_shot_horizon_patches": 32,
37
+ "symmetric_averaging": true,
38
+ "clip_nonnegative": true,
39
+ "to_univariate": false
40
+ }
results/Timestral-123M/Coastal_T_S/15T/long/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f60fb94fd29e2c24068ed40865daedaa7b753736ac83b0161293c934d1c1d040
3
+ size 6281
results/Timestral-123M/Coastal_T_S/15T/long/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:0295cca9bcce9bf4d766d52149fe8f14565f367acf41e9048f59a78356cedab9
3
+ size 83571
results/Timestral-123M/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": 23038,
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": "Timestral-123M",
34
+ "model_id": "mistralai/Timestral-123M",
35
+ "max_context": 32768,
36
+ "per_shot_horizon_patches": 32,
37
+ "symmetric_averaging": true,
38
+ "clip_nonnegative": true,
39
+ "to_univariate": false
40
+ }
results/Timestral-123M/Coastal_T_S/15T/medium/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:b15cb2b9e4bd8f33957e9e74bf8f60f6d1f1c39cb5778359fe821f122e52444c
3
+ size 14827
results/Timestral-123M/Coastal_T_S/15T/medium/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:c3371b4489bd25c45534de955d1df2893d2dcf20434c02894deb229c9e531dcc
3
+ size 91592
results/Timestral-123M/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": 23122,
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": "Timestral-123M",
34
+ "model_id": "mistralai/Timestral-123M",
35
+ "max_context": 32768,
36
+ "per_shot_horizon_patches": 32,
37
+ "symmetric_averaging": true,
38
+ "clip_nonnegative": true,
39
+ "to_univariate": false
40
+ }
results/Timestral-123M/Coastal_T_S/15T/short/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:ea663aa0e3e7d43be4fd9c356a3c6bb9dfce9e9a7a9596d04e037257504ca185
3
+ size 102927
results/Timestral-123M/Coastal_T_S/15T/short/predictions.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:e9c607c03e3e703767775db8d3ad6c12657852b142a0fef562536340ae2162ae
3
+ size 121880
results/Timestral-123M/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": 7982,
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": "Timestral-123M",
34
+ "model_id": "mistralai/Timestral-123M",
35
+ "max_context": 32768,
36
+ "per_shot_horizon_patches": 32,
37
+ "symmetric_averaging": true,
38
+ "clip_nonnegative": true,
39
+ "to_univariate": false
40
+ }
results/Timestral-123M/Coastal_T_S/20T/long/metrics.npz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:ce64ab2e631d947d46e072b724fb05196291ac7481c2284e96065baee747a39b
3
+ size 2488