b6d009ea2f49a4915fecb289496c11ae6209c27e53d139aea253747b7fdf96e3

.gitattributes

@@ -87,3 +87,6 @@ classification/UCR/PEMS-SF/PEMS-SF_TRAIN.ts filter=lfs diff=lfs merge=lfs -text
 classification/UCR/Phoneme/Phoneme_TEST.ts filter=lfs diff=lfs merge=lfs -text
 classification/UCR/PhonemeSpectra/PhonemeSpectra_TEST.ts filter=lfs diff=lfs merge=lfs -text
 classification/UCR/PhonemeSpectra/PhonemeSpectra_TRAIN.ts filter=lfs diff=lfs merge=lfs -text
+classification/UCR/SpokenArabicDigits/SpokenArabicDigits_TRAIN.ts filter=lfs diff=lfs merge=lfs -text
+classification/UCR/StarLightCurves/StarLightCurves_TEST.ts filter=lfs diff=lfs merge=lfs -text
+classification/UCR/StarLightCurves/StarLightCurves_TRAIN.ts filter=lfs diff=lfs merge=lfs -text

classification/UCR/ShakeGestureWiimoteZ/ShakeGestureWiimoteZ_TEST.ts

@@ -0,0 +1,163 @@
+## GestureWiimote
+#
+#The original data include 10 subjects, each perform 10 gestures 10 times. The gesture acquisition device is a Nintendo Wiimote remote controller with built-in three-axis accelerometer. Time series are of different lengths. There is no missing values.
+#
+#The gestures are (class label. original label - English translation): 
+#1. poteg – pick-up
+#2. shake – shake
+#3. desno – one move to the right
+#4. levo – one move to the left
+#5. gor – one move to up
+#6. dol – one move to down
+#7. kroglevo – one left circle
+#8. krogdesno – one right circle
+#9. suneknot – one move toward the screen
+#10. sunekven – one move away from the screen
+#
+#We make five datasets out of these data. 
+#
+### AllGestureWiimoteX
+#
+#Data is acceleration in x-axis dimension. Each subject performs a set of gestures multiple times. Classes are based on gestures (see class labels above).
+#
+#- Train size: 300
+#
+#- Test size: 700
+#
+#- Missing value: No
+#
+#- Number of classses: 10
+#
+#- Time series length: Vary
+#
+#Each time series is padded with NaN to the length of the longest time series in the dataset.
+#
+### AllGestureWiimoteY
+#
+#Data is acceleration in y-axis dimension. Each subject performs a set of gestures multiple times. Classes are based on gestures (see class labels above).
+#
+#- Train size: 300
+#
+#- Test size: 700
+#
+#- Missing value: No
+#
+#- Number of classses: 10
+#
+#- Time series length: Vary
+#
+#Each time series is padded with NaN to the length of the longest time series in the dataset.
+#
+### AllGestureWiimoteZ
+#
+#Data is acceleration in z-axis dimension. Classes are based on gestures (see class labels above). 
+#
+#- Train size: 300
+#
+#- Test size: 700
+#
+#- Missing value: No
+#
+#- Number of classses: 10
+#
+#- Time series length: Vary
+#
+#Each time series is padded with NaN to the length of the longest time series in the dataset.
+#
+### PickupGestureWiimoteZ
+#
+#Data is acceleration in z-axis dimension. Each subject performs "pick-up" gesture multiple times. Classes are based on subject. 
+#
+#- Train size: 50
+#
+#- Test size: 50
+#
+#- Missing value: No
+#
+#- Number of classses: 10
+#
+#- Time series length: Vary
+#
+#Each time series is padded with NaN to the length of the longest time series in the dataset.
+#
+### ShakeGestureWiimoteZ
+#
+#Data is acceleration in z-axis dimension. Each subject performs "shake" gesture multiple times. Classes are based on subject. 
+#
+#- Train size: 50
+#
+#- Test size: 50
+#
+#- Missing value: No
+#
+#- Number of classses: 10
+#
+#- Time series length: Vary
+#
+#There is nothing to infer from the order of examples in the train and test set.
+#
+#Each time series is padded with NaN to the length of the longest time series in the dataset.
+#
+#Note that data are shuffled and randomly sampled so that instances across datasets do not sychronized by dimension or subject.
+#
+#Data created by Guna Jože et al. (see [1]). Data edited by Hoang Anh Dau.
+#
+#[1] Guna, Jože, Iztok Humar, and Matevž Pogačnik. "Intuitive gesture based user identification system." Telecommunications and Signal Processing (TSP), 2012 35th International Conference on. IEEE, 2012.
+#
+@problemName ShakeGestureWiimoteZ
+@timeStamps false
+@missing false
+@univariate true
+@equalLength false
+@classLabel true 1 2 3 4 5 6 7 8 9 10
+@data
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classification/UCR/ShakeGestureWiimoteZ/ShakeGestureWiimoteZ_TRAIN.ts

@@ -0,0 +1,163 @@
+## GestureWiimote
+#
+#The original data include 10 subjects, each perform 10 gestures 10 times. The gesture acquisition device is a Nintendo Wiimote remote controller with built-in three-axis accelerometer. Time series are of different lengths. There is no missing values.
+#
+#The gestures are (class label. original label - English translation): 
+#1. poteg – pick-up
+#2. shake – shake
+#3. desno – one move to the right
+#4. levo – one move to the left
+#5. gor – one move to up
+#6. dol – one move to down
+#7. kroglevo – one left circle
+#8. krogdesno – one right circle
+#9. suneknot – one move toward the screen
+#10. sunekven – one move away from the screen
+#
+#We make five datasets out of these data. 
+#
+### AllGestureWiimoteX
+#
+#Data is acceleration in x-axis dimension. Each subject performs a set of gestures multiple times. Classes are based on gestures (see class labels above).
+#
+#- Train size: 300
+#
+#- Test size: 700
+#
+#- Missing value: No
+#
+#- Number of classses: 10
+#
+#- Time series length: Vary
+#
+#Each time series is padded with NaN to the length of the longest time series in the dataset.
+#
+### AllGestureWiimoteY
+#
+#Data is acceleration in y-axis dimension. Each subject performs a set of gestures multiple times. Classes are based on gestures (see class labels above).
+#
+#- Train size: 300
+#
+#- Test size: 700
+#
+#- Missing value: No
+#
+#- Number of classses: 10
+#
+#- Time series length: Vary
+#
+#Each time series is padded with NaN to the length of the longest time series in the dataset.
+#
+### AllGestureWiimoteZ
+#
+#Data is acceleration in z-axis dimension. Classes are based on gestures (see class labels above). 
+#
+#- Train size: 300
+#
+#- Test size: 700
+#
+#- Missing value: No
+#
+#- Number of classses: 10
+#
+#- Time series length: Vary
+#
+#Each time series is padded with NaN to the length of the longest time series in the dataset.
+#
+### PickupGestureWiimoteZ
+#
+#Data is acceleration in z-axis dimension. Each subject performs "pick-up" gesture multiple times. Classes are based on subject. 
+#
+#- Train size: 50
+#
+#- Test size: 50
+#
+#- Missing value: No
+#
+#- Number of classses: 10
+#
+#- Time series length: Vary
+#
+#Each time series is padded with NaN to the length of the longest time series in the dataset.
+#
+### ShakeGestureWiimoteZ
+#
+#Data is acceleration in z-axis dimension. Each subject performs "shake" gesture multiple times. Classes are based on subject. 
+#
+#- Train size: 50
+#
+#- Test size: 50
+#
+#- Missing value: No
+#
+#- Number of classses: 10
+#
+#- Time series length: Vary
+#
+#There is nothing to infer from the order of examples in the train and test set.
+#
+#Each time series is padded with NaN to the length of the longest time series in the dataset.
+#
+#Note that data are shuffled and randomly sampled so that instances across datasets do not sychronized by dimension or subject.
+#
+#Data created by Guna Jože et al. (see [1]). Data edited by Hoang Anh Dau.
+#
+#[1] Guna, Jože, Iztok Humar, and Matevž Pogačnik. "Intuitive gesture based user identification system." Telecommunications and Signal Processing (TSP), 2012 35th International Conference on. IEEE, 2012.
+#
+@problemName ShakeGestureWiimoteZ
+@timeStamps false
+@missing false
+@univariate true
+@equalLength false
+@classLabel true 1 2 3 4 5 6 7 8 9 10
+@data
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classification/UCR/SmoothSubspace/SmoothSubspace_TEST.ts

@@ -0,0 +1,184 @@
+## SmoothSubspace dataset
+#
+#The data was originally intended for testing whether a clustering algorithm is able to extract smooth subspaces for clustering time series data [1]. 
+#
+#There are 3 classes corresponding to which cluster the time series belong to. Each time series contain a continuous subspace spanning over 5 continuous time stamps. 
+#- For cluster 1, it is from time stamp 1-5 
+#- For cluster 2, it is from time stamp 6-10
+#- For cluster 3, it is from time stamp 11-15. 
+#
+#The rest of the time series are randomly generated.
+#
+#Train size: 150
+#
+#Test size: 150
+#
+#Missing value: No
+#
+#Number of classses: 3
+#
+#Time series length: 15
+#
+#There is nothing to infer from the order of examples in the train and test set.
+#
+#Data created by Xiaohui Huang et al. (see [1]). Data edited by Hoang Anh Dau.
+#
+#[1] Huang, Xiaohui, et al. "Time series k-means: A new k-means type smooth subspace clustering for time series data." Information Sciences 367 (2016): 1-13.
+@problemName SmoothSubspace
+@timeStamps false
+@missing false
+@univariate true
+@equalLength true
+@seriesLength 15
+@classLabel true 1 2 3
+@data
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classification/UCR/SmoothSubspace/SmoothSubspace_TRAIN.ts

@@ -0,0 +1,184 @@
+## SmoothSubspace dataset
+#
+#The data was originally intended for testing whether a clustering algorithm is able to extract smooth subspaces for clustering time series data [1]. 
+#
+#There are 3 classes corresponding to which cluster the time series belong to. Each time series contain a continuous subspace spanning over 5 continuous time stamps. 
+#- For cluster 1, it is from time stamp 1-5 
+#- For cluster 2, it is from time stamp 6-10
+#- For cluster 3, it is from time stamp 11-15. 
+#
+#The rest of the time series are randomly generated.
+#
+#Train size: 150
+#
+#Test size: 150
+#
+#Missing value: No
+#
+#Number of classses: 3
+#
+#Time series length: 15
+#
+#There is nothing to infer from the order of examples in the train and test set.
+#
+#Data created by Xiaohui Huang et al. (see [1]). Data edited by Hoang Anh Dau.
+#
+#[1] Huang, Xiaohui, et al. "Time series k-means: A new k-means type smooth subspace clustering for time series data." Information Sciences 367 (2016): 1-13.
+@problemName SmoothSubspace
+@timeStamps false
+@missing false
+@univariate true
+@equalLength true
+@seriesLength 15
+@classLabel true 1 2 3
+@data
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classification/UCR/SonyAIBORobotSurface1/SonyAIBORobotSurface1_TRAIN.ts

@@ -0,0 +1,32 @@
+#This dataset was donated by Manuela Veloso and Douglas Vail of
+#Carnegie Mellon University. The robot has roll/pitch/yaw
+#accelerometers. This data is just the X-axis. The task is to detect
+#the surface being walked on (cement or carpet for Sony1).
+@problemName SonyAIBORobotSurface1
+@timeStamps false
+@missing false
+@univariate true
+@equalLength true
+@seriesLength 70
+@classLabel true 1 2
+@data
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classification/UCR/SonyAIBORobotSurface2/SonyAIBORobotSurface2_TRAIN.ts

@@ -0,0 +1,35 @@
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+@missing false
+@univariate true
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