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ECGFiveDays/ECGFiveDays_TRAIN.ts

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+#ECGFiveDays is an ECG set from physionet formatted by Eamonn Keogh. 
+@problemName ECGFiveDays
+@timeStamps false
+@missing false
+@univariate true
+@equalLength true
+@seriesLength 136
+@classLabel true 1 2
+@data
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MoteStrain/MoteStrain_TRAIN.ts

@@ -0,0 +1,35 @@
+#Sensor data originally from Carlos Guestrin (CMU),used in the paper
+#<em>Online Latent Variable Detection in Sensor Networks</em> and
+#formatted for classification by Eamonn Keogh. The task is to
+#distinguish between sensor q8calibHumid (a humidity measure) and
+#sensor q8calibHumTemp (temperature). The data has dropouts. In the
+#original data the dropouts have value 0. This means they are
+#difficult to detect with normalised data.
+@problemName MoteStrain
+@timeStamps false
+@missing false
+@univariate true
+@equalLength true
+@seriesLength 84
+@classLabel true 1 2
+@data
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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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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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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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UMD/UMD_TRAIN.ts

@@ -0,0 +1,63 @@
+## UMD dataset
+#
+#UMD is a synthetic data set with 3 classes: one class is characterized by a small up bell arising at the initial or final period (Up); one does not have any bell (Middle); one has a small down bell arising at the initial or final period (Down).
+#
+#- Class 1: Up
+#- Class 2: Middle
+#- Class 3: Down
+#
+#Train size: 36
+#
+#Test size: 144
+#
+#Missing value: No
+#
+#Number of classses: 3
+#
+#Time series length: 150
+#
+#Data source: Université Joseph Fourier (Grenoble I), Laboratoire d’informatique de Grenoble(LIG), Equipe AMA
+@problemName UMD
+@timeStamps false
+@missing false
+@univariate true
+@equalLength true
+@seriesLength 150
+@classLabel true 1 2 3
+@data
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