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.gitattributes

@@ -57,3 +57,9 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+UEA/UWaveGestureLibraryZ/UWaveGestureLibraryZ_TEST.ts filter=lfs diff=lfs merge=lfs -text
+UEA/StarLightCurves/StarLightCurves_TRAIN.ts filter=lfs diff=lfs merge=lfs -text
+UEA/UWaveGestureLibraryY/UWaveGestureLibraryY_TEST.ts filter=lfs diff=lfs merge=lfs -text
+UEA/Yoga/Yoga_TEST.ts filter=lfs diff=lfs merge=lfs -text
+UEA/FordA/FordA_TRAIN.ts filter=lfs diff=lfs merge=lfs -text
+UEA/EMOPain/EMOPain_TEST.ts filter=lfs diff=lfs merge=lfs -text

SMD/SMD_train.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ef3a4a4e9c87c939c23e0cdf1190c18a1d41ef1aadb5d6c5ae909514332ef791
+size 107677688

UEA/BME/BME_TRAIN.ts

@@ -0,0 +1,59 @@
+## BME dataset
+#
+#BME is a synthetic data set with three classes:  one class is characterized by a small positive bell arising at the initial period (Begin), one does not have any bell (Middle), one has a positive bell arising at the final period (End).
+#
+#All series are constituted by a central plate. The central plates may be positive or negative. The discriminant is the presence or absence of a positive peak, or at the beginning of series or at the end.
+#
+#- Class 1: Begin
+#- Class 2: Middle
+#- Class 3: End
+#
+#Train size: 30
+#
+#Test size: 150
+#
+#Missing value: No
+#
+#Number of classses: 3
+#
+#Time series length: 128
+#
+#Data source: Université Joseph Fourier (Grenoble I), Laboratoire d’informatique de Grenoble(LIG), Equipe AMA
+@problemName BME
+@timeStamps false
+@missing false
+@univariate true
+@equalLength true
+@seriesLength 128
+@classLabel true 1 2 3
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UEA/Blink/Blink.txt

@@ -0,0 +1,7 @@
+EEG dataset based on classifying between short and long blinks. Each of the subjects was asked to blink for a duration of 2 seconds, as either a short or long blink. 
+Each sample is 510 values long, with 4 EEG channels, representing 2 seconds sampled at 255Hz. The data were collected in 20 trials of six patients, 
+with 50 blinks per experiment. One of the long blink data files in the original repo (https://github.com/Kelvin9811/EEG-Blink-dataset) was a duplicate, so we have removed this.
+leaving 950 cases. The first five long and short blink trials are used for the train data (250 long blink, 250 short blink). The remaining 200 long blink and 250 short blink constitute the test data set.  
+
+The data was used in the following publication. 
+https://ieeexplore.ieee.org/abstract/document/9590711

UEA/Blink/Blink.zip

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+oid sha256:c16e1c33ee5594fc3fb2a16f98d2a12e0f9cb90a8b8f3eaf1786e11476705b01
+size 10164793

UEA/DodgerLoopWeekend/DodgerLoopWeekend_TRAIN.ts

@@ -0,0 +1,101 @@
+## DodgersLoop dataset
+#
+#The traffic data are collected with the loop sensor installed on ramp for the 101 North freeway in Los Angeles. This location is close to Dodgers Stadium; therefore the traffic is affected by volumne of visitors to the stadium.
+#
+#We make three datasets out of these data.
+#
+### DodgersLoopDay
+#
+#The classes are days of the week.
+#
+#- Class 1: Sunday
+#- Class 2: Monday
+#- Class 3: Tuesday
+#- Class 4: Wednesday
+#- Class 5: Thursday 
+#- Class 6: Friday
+#- Class 7: Saturday 
+#
+#Train size: 78
+#
+#Test size: 80
+#
+#Number of classes: 7
+#
+#Missing value: Yes
+#
+#Time series length: 288
+#
+#Missing values are represented with NaN.
+#
+### DodgersLoopWeekend
+#
+#- Class 1: Weekday
+#- Class 2: Weekend
+#
+#Train size: 20
+#
+#Test size: 138
+#
+#Number of classes: 2
+#
+#Missing value: Yes
+#
+#Time series length: 288
+#
+#Missing values are represented with NaN.
+#
+### DodgersLoopGame
+#
+#- Class 1: Normal day
+#- Class 2: Game day
+#
+#Train size: 20
+#
+#Test size: 138
+#
+#Number of classes: 2
+#
+#Missing value: Yes
+#
+#Time series length: 288
+#
+#There is nothing to infer from the order of examples in the train and test set.
+#
+#Missing values are represented with NaN.
+#
+#Data created by Ihler, Alexander, Jon Hutchins, and Padhraic Smyth (see [1][2][3]). Data edited by Chin-Chia Michael Yeh. 
+#
+#[1] Ihler, Alexander, Jon Hutchins, and Padhraic Smyth. "Adaptive event detection with time-varying poisson processes." Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2006.
+#
+#[2] “UCI Machine Learning Repository: Dodgers Loop Sensor Data Set.” UCI Machine Learning Repository, archive.ics.uci.edu/ml/datasets/dodgers+loop+sensor.
+#
+#[3] “Caltrans PeMS.” Caltrans, pems.dot.ca.gov/.
+@problemName DodgerLoopWeekend
+@timeStamps false
+@missing true
+@univariate true
+@equalLength true
+@seriesLength 288
+@classLabel true 1 2
+@data
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UEA/EMOPain/EMOPain_TEST.ts

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UEA/ERing/ERing_TRAIN.ts

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+#This data is generated with a prototype finger ring, called eRing [1], that can be used to detect hand and finger gestures. eRing uses electric field sensing. In this paper we present an early prototype to 
+#describe finger- and hand-gestures. The data set we used is the D data set used for Finger Posture Recognition. There are six classes for six postures involving the thumb, the index finger, and the middle finger.
+#The data is four dimensional. Each series contains 65 observations. Each series is a measurement from an electrode which varies dependent on the distance to the hand.  
+#
+#[1] M. Wilhelm, D. Krakowczyk, F. Trollmann, S Albayrak. eRing: multiple finger gesture recognition with one ring using an electric field, Proceedings of the 2nd international Workshop on Sensor-based Activity Recognition and Interaction, 2015
+#
+@problemName ERing
+@timeStamps false
+@missing false
+@univariate false
+@dimensions 4
+@equalLength true
+@seriesLength 65
+@classLabel true 1 2 3 4 5 6
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