aeon-toolkit · TonyBagnall · Sep 24, 2026 · Sep 24, 2026 · Sep 24, 2026 · Sep 24, 2026
diff --git a/README.md b/README.md
@@ -18,7 +18,7 @@ The **Multiverse** is an expanded archive for multivariate time series classific
 datasets from the original UEA MTSC archive, newer MTSC collections, donated 
 standalone datasets, and associated benchmark results into a single open repository.
 
-**Leaderboards:** [Multiverse-core](#multiverse-core-leaderboard) (66 datasets) ·
+**Leaderboards:** [Multiverse-core](#multiverse-core-leaderboard) (65 datasets) ·
 [Full archive](docs/leaderboard_full.md) (the paper's 100 datasets) ·
 [EEG](docs/leaderboard_eeg.md) (26 datasets)
 
@@ -37,40 +37,40 @@ The current paper version describes:
 
 - 133 unique MTSC problems
 - 147 released datasets when preprocessing variants are included
-- a curated 66 dataset subset, **Multiverse-core (MV-core)**, for algorithm benchmarking
+- a curated 65 dataset subset, **Multiverse-core (MV-core)**, for algorithm benchmarking
 
 ### Multiverse-core leaderboard
 
 <!-- LEADERBOARD:START -->
 | # | Estimator | Accuracy rank | Accuracy | Balanced accuracy | AUROC | F1 | Log loss &darr; | Sensitivity | Specificity |
 |---|---|---|---|---|---|---|---|---|---|
-| 1 | HC2 | **7.52** | **0.7936** | **0.7528** | **0.8949** | **0.7337** | **0.5307** | 0.7499 | **0.8018** |
-| 2 | MRHydra | 9.02 | 0.7806 | 0.7503 | 0.8039 | 0.7293 | 7.9077 | **0.7584** | 0.7794 |
-| 3 | RDST | 9.34 | 0.7746 | 0.7371 | 0.7933 | 0.7113 | 8.1235 | 0.7199 | 0.7924 |
-| 4 | RIST | 9.84 | 0.7750 | 0.7428 | 0.8685 | 0.7196 | 0.6087 | 0.7424 | 0.7758 |
-| 5 | CIF | 10.19 | 0.7782 | 0.7459 | 0.8847 | 0.7273 | 0.6376 | 0.7473 | 0.7799 |
-| 6 | DrCIF | 10.36 | 0.7731 | 0.7401 | 0.8743 | 0.7202 | 0.6370 | 0.7396 | 0.7752 |
-| 7 | QUANT | 10.87 | 0.7667 | 0.7370 | 0.8693 | 0.7177 | 0.7198 | 0.7476 | 0.7594 |
-| 8 | Arsenal | 10.99 | 0.7683 | 0.7309 | 0.8425 | 0.7096 | 3.5868 | 0.7293 | 0.7777 |
-| 9 | ROCKET | 11.14 | 0.7698 | 0.7328 | 0.7899 | 0.7088 | 8.2968 | 0.7245 | 0.7811 |
-| 10 | LITETime-MV | 11.16 | 0.7505 | 0.7274 | 0.8511 | 0.6864 | 1.3223 | 0.7113 | 0.7707 |
-| 11 | STSF | 11.23 | 0.7735 | 0.7486 | 0.8722 | 0.7188 | 0.6639 | 0.7417 | 0.7862 |
-| 12 | H-InceptionTime | 11.76 | 0.7418 | 0.7179 | 0.8460 | 0.6881 | 1.3451 | 0.7233 | 0.7428 |
-| 13 | ConvTran | 12.95 | 0.7485 | 0.7152 | 0.8545 | 0.6892 | 0.8930 | 0.7251 | 0.7401 |
-| 14 | Catch22 | 13.09 | 0.7495 | 0.7189 | 0.8622 | 0.6977 | 0.6971 | 0.7263 | 0.7459 |
-| 15 | PatchMTSC | 13.16 | 0.7478 | 0.6996 | 0.8276 | 0.6723 | 0.7796 | 0.7018 | 0.7425 |
-| 16 | DisjointCNN | 13.44 | 0.7267 | 0.7021 | 0.8267 | 0.6662 | 2.1025 | 0.6857 | 0.7344 |
-| 17 | STC | 13.84 | 0.7527 | 0.7109 | 0.8633 | 0.6843 | 0.6397 | 0.7072 | 0.7629 |
-| 18 | TSF | 14.06 | 0.7434 | 0.7142 | 0.8570 | 0.6918 | 0.9812 | 0.7105 | 0.7536 |
-| 19 | TDE | 15.01 | 0.7266 | 0.6811 | 0.8344 | 0.6488 | 0.8411 | 0.6800 | 0.7351 |
-| 20 | TS2Vec | 15.41 | 0.7203 | 0.6798 | 0.8005 | 0.6556 | 0.7806 | 0.6886 | 0.7154 |
-| 21 | Summary | 16.66 | 0.6886 | 0.6585 | 0.8135 | 0.6357 | 0.9475 | 0.6661 | 0.6901 |
-| 22 | XCM | 16.78 | 0.6707 | 0.6357 | 0.7914 | 0.5827 | 2.1902 | 0.6233 | 0.6824 |
-| 23 | TimesNet | 17.22 | 0.7039 | 0.6685 | 0.8236 | 0.6437 | 1.2796 | 0.6824 | 0.6968 |
-| 24 | TimesURL | 17.28 | 0.6973 | 0.6538 | 0.7828 | 0.6099 | 0.9919 | 0.6345 | 0.7050 |
-| 25 | Dummy | 22.66 | 0.3802 | 0.3105 | 0.5000 | 0.1804 | 1.3681 | 0.3192 | 0.3882 |
-
-Average over the 58 Multiverse-core datasets with results for every estimator on every metric, ordered by average accuracy rank. Best in each column in bold.
+| 1 | HC2 | **7.40** | **0.7938** | **0.7543** | **0.8951** | **0.7378** | **0.5316** | 0.7570 | **0.7987** |
+| 2 | MRHydra | 8.77 | 0.7838 | 0.7529 | 0.8074 | 0.7336 | 7.7909 | **0.7609** | 0.7828 |
+| 3 | RDST | 9.18 | 0.7752 | 0.7396 | 0.7968 | 0.7179 | 8.1012 | 0.7289 | 0.7891 |
+| 4 | RIST | 9.82 | 0.7742 | 0.7430 | 0.8677 | 0.7213 | 0.6123 | 0.7469 | 0.7724 |
+| 5 | CIF | 10.16 | 0.7775 | 0.7463 | 0.8843 | 0.7293 | 0.6414 | 0.7521 | 0.7767 |
+| 6 | DrCIF | 10.37 | 0.7722 | 0.7402 | 0.8734 | 0.7216 | 0.6413 | 0.7437 | 0.7718 |
+| 7 | Arsenal | 10.80 | 0.7709 | 0.7336 | 0.8469 | 0.7145 | 3.6382 | 0.7340 | 0.7792 |
+| 8 | QUANT | 10.92 | 0.7651 | 0.7362 | 0.8678 | 0.7177 | 0.7263 | 0.7506 | 0.7554 |
+| 9 | ROCKET | 11.04 | 0.7701 | 0.7345 | 0.7927 | 0.7134 | 8.2862 | 0.7315 | 0.7781 |
+| 10 | LITETime-MV | 11.24 | 0.7483 | 0.7260 | 0.8493 | 0.6851 | 1.3395 | 0.7129 | 0.7668 |
+| 11 | STSF | 11.41 | 0.7699 | 0.7447 | 0.8700 | 0.7145 | 0.6742 | 0.7379 | 0.7827 |
+| 12 | H-InceptionTime | 11.89 | 0.7379 | 0.7137 | 0.8434 | 0.6837 | 1.3662 | 0.7192 | 0.7389 |
+| 13 | Catch22 | 12.90 | 0.7539 | 0.7224 | 0.8680 | 0.7027 | 0.6972 | 0.7290 | 0.7507 |
+| 14 | ConvTran | 13.12 | 0.7446 | 0.7110 | 0.8520 | 0.6846 | 0.9070 | 0.7219 | 0.7356 |
+| 15 | PatchMTSC | 13.31 | 0.7443 | 0.6959 | 0.8247 | 0.6682 | 0.7905 | 0.6996 | 0.7381 |
+| 16 | DisjointCNN | 13.64 | 0.7224 | 0.6975 | 0.8239 | 0.6611 | 2.1344 | 0.6814 | 0.7299 |
+| 17 | STC | 13.86 | 0.7518 | 0.7106 | 0.8637 | 0.6853 | 0.6428 | 0.7104 | 0.7599 |
+| 18 | TSF | 14.15 | 0.7419 | 0.7138 | 0.8558 | 0.6926 | 0.9914 | 0.7141 | 0.7498 |
+| 19 | TDE | 14.94 | 0.7272 | 0.6843 | 0.8379 | 0.6601 | 0.8450 | 0.6919 | 0.7304 |
+| 20 | TS2Vec | 15.41 | 0.7192 | 0.6790 | 0.7995 | 0.6562 | 0.7869 | 0.6907 | 0.7125 |
+| 21 | Summary | 16.52 | 0.6936 | 0.6614 | 0.8194 | 0.6393 | 0.9505 | 0.6648 | 0.6979 |
+| 22 | XCM | 16.80 | 0.6691 | 0.6359 | 0.7915 | 0.5859 | 2.1699 | 0.6299 | 0.6769 |
+| 23 | TimesURL | 17.23 | 0.6975 | 0.6565 | 0.7833 | 0.6206 | 0.9962 | 0.6456 | 0.6998 |
+| 24 | TimesNet | 17.41 | 0.7001 | 0.6647 | 0.8209 | 0.6399 | 1.2957 | 0.6804 | 0.6919 |
+| 25 | Dummy | 22.70 | 0.3748 | 0.3072 | 0.5000 | 0.1836 | 1.3810 | 0.3248 | 0.3774 |
+
+Average over the 57 Multiverse-core datasets with results for every estimator on every metric, ordered by average accuracy rank. Best in each column in bold.
 <!-- LEADERBOARD:END -->
 
 Rebuilt with `python -m multiverse.experiments.tables`, which also writes a sortable

diff --git a/docs/classifiers.md b/docs/classifiers.md
@@ -260,14 +260,17 @@ It also seeds from ``random_state`` directly rather than through
 ``check_random_state``; that one is not a fidelity constraint, since TSLib simply sets a
 global seed in ``run.py``, and it could be unified with the other two ports.
 
-TimesNet reproduces TSLib's learning rate schedule, which the classification loop
-applies every five epochs. With the default ``lr_adjust="type1"`` the rate becomes
-``learning_rate * 0.5 ** (epoch - 1)`` at epochs 5, 10, 15 and so on, so from the
-published ``learning_rate=0.001`` it falls to 6.3e-5 by epoch 5 and 6.1e-8 by epoch 15:
-over a default 30 epoch run the model is effectively frozen well before the end. Pass
-``lr_adjust=None`` to train at a constant rate instead. Omitting this schedule, as
-earlier versions of this port did, is a materially different optimisation and makes
-results incomparable with the published ones.
+TimesNet trains at a constant learning rate by default, ``lr_adjust=None``, which
+departs from TSLib. TSLib defaults to ``lradj="type1"``, applied every five epochs: the
+rate becomes ``learning_rate * 0.5 ** (epoch - 1)`` at epochs 5, 10, 15 and so on, so
+from the published ``learning_rate=0.001`` it falls to 6.3e-5 by epoch 5 and 6.1e-8 by
+epoch 15, and over a 30 epoch run the model is effectively frozen well before the end.
+That is harmless in TSLib only because it selects the retained epoch on the test set,
+keeping an early epoch from before the collapse. This port selects on a held-out split
+of the training data, so with the schedule on it keeps a model that has stopped
+learning: on ERing, 0.933 without the schedule against 0.578 with it, at otherwise
+identical settings. Pass ``lr_adjust="type1"`` to reproduce TSLib. The published
+TimesNet results here are from the default, constant rate.
 
 ### Equivalence testing
 

diff --git a/docs/datasets.md b/docs/datasets.md
@@ -83,7 +83,7 @@ The project uses the archive collections exposed by aeon:
 ```python
 from aeon.datasets.tsc_datasets import multiverse_core, multiverse2026, eeg2026
 
-print(len(multiverse_core))  # 66
+print(len(multiverse_core))  # 65
 print(len(multiverse2026))   # 133
 print(len(eeg2026))          # 28
 ```
@@ -94,6 +94,11 @@ very simple and zero-information datasets, and has a useful spread of dataset si
 series lengths. The current benchmark results use this core list unless stated
 otherwise.
 
+BenzeneConcentration_disc has been removed from Multiverse-core, since the version the
+results were run on has a channel that is a deterministic function of the target
+([version 2](https://zenodo.org/records/21871727) drops it). aeon versions that
+predate the change still list it, and 66 datasets. It stays in `multiverse2026`.
+
 The EEG collection is a separate classification archive used for EEG-specific
 experiments. It is based on [aeon-neuro](https://github.com/aeon-toolkit/aeon-neuro).
 

diff --git a/docs/leaderboard_eeg.md b/docs/leaderboard_eeg.md
@@ -58,8 +58,9 @@ datasets, so are not ranked above. The same gaps are in
 ## EEG archive study results
 
 These are the averaged results held in [`results/eeg`](../results/eeg), for twelve
-estimators including the EEG-specific CSP-SVM, R-KNN and R-MDM. They come from a separate
-study, not the resample-0 runs above, so the two tables are not directly comparable.
+estimators including the EEG-specific CSP-SVM, R-KNN and R-MDM. They come from the
+[EEG archive paper](https://openreview.net/pdf?id=oPQpQlsfD0), not the resample-0 runs
+above, so the two tables are not directly comparable.
 Their 26 datasets also differ: they include the univariate Sleep and leave out
 FeedbackButton.
 

diff --git a/multiverse/experiments/tables.py b/multiverse/experiments/tables.py
@@ -41,6 +41,7 @@
 
 import base64
 import io
+import re
 from datetime import date
 from html import escape
 from pathlib import Path
@@ -369,6 +370,13 @@ def _missing_by_estimator(frames, estimators, common, datasets):
         "cannot complete the archive within the walltime available: exceeded the "
         "limit on BIDMC32HR_disc, with no result recorded for BIDMC32SpO2_disc"
     ),
+    "MUSE": (
+        "cannot complete the archive as implemented: on STEW and Skoda its word "
+        "bag outgrows the 32-bit sparse indices scikit-learn's classifier "
+        "accepts, which no memory allocation fixes, and on USCActivity its chi2 "
+        "selection needs 287 GiB. MotorImagery exhausted 16 GB and awaits a "
+        "rerun at the configuration of its other results"
+    ),
     "CIF-500": (
         "the 500-tree configuration run for the Multiverse archive paper's "
         "full-archive benchmark, where it is reported as CIF. This table reports "
@@ -392,7 +400,7 @@ def _missing_by_estimator(frames, estimators, common, datasets):
 # Datasets held out of the collection, and why. Distinct from the datasets an
 # individual estimator is missing: these are ones no amount of scheduling will
 # close, so leaving them in the denominator only makes the scored fraction look
-# like a queue that is still draining.
+# like a queue that is still draining. A URL in a reason is rendered as a link.
 DEFERRED_DATASETS = {
     "AustraliaRainfall_disc": (
         "112186 cases, and three estimators fail on it for reasons compute cannot "
@@ -404,15 +412,26 @@ def _missing_by_estimator(frames, estimators, common, datasets):
         "the series are length 8 and MRHydra requires at least 9, so the dataset "
         "cannot complete while MRHydra is a column"
     ),
+    "BenzeneConcentration_disc": (
+        "removed from Multiverse-core. The results here are on version 1, whose "
+        "PT08.S2 channel is a deterministic function of the target; version 2 "
+        "drops it, on the advice of the original UCI Air Quality donors: "
+        "https://zenodo.org/records/21871727"
+    ),
 }
 
 
+def _link_urls(text: str) -> str:
+    """Turn each https URL in already escaped text into a link."""
+    return re.sub(r"(https://[^\s<]+)", r'<a href="\1">\1</a>', text)
+
+
 def _deferred_html() -> str:
     """Name the datasets held out of the collection, and why."""
     if not DEFERRED_DATASETS:
         return ""
     items = "".join(
-        f"<li><b>{escape(name)}</b> &mdash; {escape(reason)}.</li>"
+        f"<li><b>{escape(name)}</b> &mdash; {_link_urls(escape(reason))}.</li>"
         for name, reason in DEFERRED_DATASETS.items()
     )
     return (

diff --git a/results/multiverse/LiteTIME/LiteTIME_accuracy.csv b/results/multiverse/LiteTIME/LiteTIME_accuracy.csv
@@ -7,6 +7,7 @@ AsphaltRegularityCoordinates,0.9893475366178429
 AtrialFibrillation,0.13333333333333333
 AustraliaRainfall_disc,0.7788107568478193
 AutomotiveRoadTrials,0.7922077922077922
+BasicMotions,1.0
 BeijingPM10Quality_disc,0.8195324881141046
 BeijingPM25Quality_disc,0.8785657686212361
 BenzeneConcentration_disc,0.9101297695138485
@@ -25,6 +26,7 @@ Epilepsy,0.9927536231884058
 EthanolConcentration,0.21673003802281368
 EyesOpenShut,0.42857142857142855
 FaceDetection,0.6677071509648127
+FingerMovements,0.57
 FordChallenge,0.7440033085194375
 HandMovementDirection,0.35135135135135137
 Handwriting,0.5988235294117648
@@ -52,6 +54,7 @@ PhotoStimulation,0.2222222222222222
 RacketSports,0.875
 STEW,0.6285072951739619
 SelfRegulationSCP1,0.7952218430034129
+SelfRegulationSCP2,0.5166666666666667
 Skoda,0.9639193491333569
 SpokenArabicDigits,0.9931787175989086
 StandWalkJump,0.4666666666666667

diff --git a/results/multiverse/LiteTIME/LiteTIME_auroc.csv b/results/multiverse/LiteTIME/LiteTIME_auroc.csv
@@ -7,6 +7,7 @@ AsphaltRegularityCoordinates,0.9969922678584096
 AtrialFibrillation,0.3733333333333333
 AustraliaRainfall_disc,0.8600906211975378
 AutomotiveRoadTrials,0.8566243194192378
+BasicMotions,1.0
 BeijingPM10Quality_disc,0.8716951140762138
 BeijingPM25Quality_disc,0.9306582924184476
 BenzeneConcentration_disc,0.9668722682717931
@@ -25,6 +26,7 @@ Epilepsy,0.9997889002947116
 EthanolConcentration,0.5087754852573134
 EyesOpenShut,0.3900226757369614
 FaceDetection,0.7264014811360014
+FingerMovements,0.6182472989195679
 FordChallenge,0.853777545661815
 HandMovementDirection,0.6642613723122198
 Handwriting,0.9410721040377091
@@ -52,6 +54,7 @@ PhotoStimulation,0.3781196581196581
 RacketSports,0.9654502871283713
 STEW,0.8105854734525703
 SelfRegulationSCP1,0.9244012673562576
+SelfRegulationSCP2,0.5190123456790123
 Skoda,0.9978507333547619
 SpokenArabicDigits,0.9999843743573092
 StandWalkJump,0.5666666666666667

diff --git a/results/multiverse/LiteTIME/LiteTIME_balacc.csv b/results/multiverse/LiteTIME/LiteTIME_balacc.csv
@@ -7,6 +7,7 @@ AsphaltRegularityCoordinates,0.989501312335958
 AtrialFibrillation,0.13333333333333333
 AustraliaRainfall_disc,0.423103469854606
 AutomotiveRoadTrials,0.7912885662431942
+BasicMotions,1.0
 BeijingPM10Quality_disc,0.7767887096835823
 BeijingPM25Quality_disc,0.8600541587311511
 BenzeneConcentration_disc,0.8996512888796334
@@ -25,6 +26,7 @@ Epilepsy,0.9926470588235294
 EthanolConcentration,0.21643356643356643
 EyesOpenShut,0.42857142857142855
 FaceDetection,0.6677071509648127
+FingerMovements,0.5704281712685074
 FordChallenge,0.7706758629262975
 HandMovementDirection,0.3726190476190476
 Handwriting,0.5970573977871463
@@ -52,6 +54,7 @@ PhotoStimulation,0.21919191919191916
 RacketSports,0.8851744186046512
 STEW,0.6285072951739619
 SelfRegulationSCP1,0.795871773366881
+SelfRegulationSCP2,0.5166666666666666
 Skoda,0.9628853708859495
 SpokenArabicDigits,0.9931818181818182
 StandWalkJump,0.4666666666666666

diff --git a/results/multiverse/LiteTIME/LiteTIME_f1.csv b/results/multiverse/LiteTIME/LiteTIME_f1.csv
@@ -7,6 +7,7 @@ AsphaltRegularityCoordinates,0.9893048128342246
 AtrialFibrillation,0.10822510822510822
 AustraliaRainfall_disc,0.7687181575723717
 AutomotiveRoadTrials,0.6521739130434783
+BasicMotions,1.0
 BeijingPM10Quality_disc,0.6837903505727178
 BeijingPM25Quality_disc,0.8019386106623586
 BenzeneConcentration_disc,0.8575813382443217
@@ -25,6 +26,7 @@ Epilepsy,0.9927478549282573
 EthanolConcentration,0.20782350943667244
 EyesOpenShut,0.42857142857142855
 FaceDetection,0.6762510367708046
+FingerMovements,0.5742574257425742
 FordChallenge,0.721213031076415
 HandMovementDirection,0.3511120103671958
 Handwriting,0.5756336373055114
@@ -52,6 +54,7 @@ PhotoStimulation,0.2236983360171766
 RacketSports,0.8749272515640915
 STEW,0.43442973088423753
 SelfRegulationSCP1,0.8275862068965517
+SelfRegulationSCP2,0.47904191616766467
 Skoda,0.9637774526594892
 SpokenArabicDigits,0.9931705004917735
 StandWalkJump,0.3904761904761905

diff --git a/results/multiverse/LiteTIME/LiteTIME_logloss.csv b/results/multiverse/LiteTIME/LiteTIME_logloss.csv
@@ -7,6 +7,7 @@ AsphaltRegularityCoordinates,0.0633709328876837
 AtrialFibrillation,3.830235855233876
 AustraliaRainfall_disc,0.5082520114132473
 AutomotiveRoadTrials,0.7647017220924607
+BasicMotions,0.0031483720772545803
 BeijingPM10Quality_disc,1.2811467872585638
 BeijingPM25Quality_disc,1.0247847706659101
 BenzeneConcentration_disc,0.24977842997786107
@@ -25,6 +26,7 @@ Epilepsy,0.026125259800652865
 EthanolConcentration,2.8835702178775393
 EyesOpenShut,1.7713254378257652
 FaceDetection,1.4778285212115687
+FingerMovements,1.4255986332995871
 FordChallenge,0.6414512694285133
 HandMovementDirection,1.605375679310031
 Handwriting,1.5516101478469824
@@ -52,6 +54,7 @@ PhotoStimulation,3.861648651725385
 RacketSports,0.4355536405511637
 STEW,2.914516308229351
 SelfRegulationSCP1,0.8235472491231484
+SelfRegulationSCP2,1.01696868038982
 Skoda,0.2011567722867202
 SpokenArabicDigits,0.01492038685305996
 StandWalkJump,2.9512630582435757

diff --git a/results/multiverse/LiteTIME/LiteTIME_sensitivity.csv b/results/multiverse/LiteTIME/LiteTIME_sensitivity.csv
@@ -7,6 +7,7 @@ AsphaltRegularityCoordinates,1.0
 AtrialFibrillation,0.13333333333333333
 AustraliaRainfall_disc,0.7788107568478193
 AutomotiveRoadTrials,0.7894736842105263
+BasicMotions,1.0
 BeijingPM10Quality_disc,0.6755829903978052
 BeijingPM25Quality_disc,0.8132372214941023
 BenzeneConcentration_disc,0.8720349563046192
@@ -25,6 +26,7 @@ Epilepsy,0.9927536231884058
 EthanolConcentration,0.21673003802281368
 EyesOpenShut,0.42857142857142855
 FaceDetection,0.6940976163450624
+FingerMovements,0.5918367346938775
 FordChallenge,0.8788876692279546
 HandMovementDirection,0.35135135135135137
 Handwriting,0.5988235294117648
@@ -52,6 +54,7 @@ PhotoStimulation,0.2222222222222222
 RacketSports,0.875
 STEW,0.28535353535353536
 SelfRegulationSCP1,0.9863013698630136
+SelfRegulationSCP2,0.4444444444444444
 Skoda,0.9639193491333569
 SpokenArabicDigits,0.9931787175989086
 StandWalkJump,0.4666666666666667

diff --git a/results/multiverse/LiteTIME/LiteTIME_specificity.csv b/results/multiverse/LiteTIME/LiteTIME_specificity.csv
@@ -7,6 +7,7 @@ AsphaltRegularityCoordinates,0.979002624671916
 AtrialFibrillation,0.13333333333333333
 AustraliaRainfall_disc,0.7788107568478193
 AutomotiveRoadTrials,0.7931034482758621
+BasicMotions,1.0
 BeijingPM10Quality_disc,0.8779944289693593
 BeijingPM25Quality_disc,0.9068710959681999
 BenzeneConcentration_disc,0.9272676214546476
@@ -25,6 +26,7 @@ Epilepsy,0.9927536231884058
 EthanolConcentration,0.21673003802281368
 EyesOpenShut,0.42857142857142855
 FaceDetection,0.641316685584563
+FingerMovements,0.5490196078431373
 FordChallenge,0.6624640566246406
 HandMovementDirection,0.35135135135135137
 Handwriting,0.5988235294117648
@@ -52,6 +54,7 @@ PhotoStimulation,0.2222222222222222
 RacketSports,0.875
 STEW,0.9716610549943884
 SelfRegulationSCP1,0.6054421768707483
+SelfRegulationSCP2,0.5888888888888889
 Skoda,0.9639193491333569
 SpokenArabicDigits,0.9931787175989086
 StandWalkJump,0.4666666666666667