Datasets:

sktime
/

tsc-datasets

@@ -57,18 +57,26 @@ 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
-Mallat/Mallat_TEST.ts filter=lfs diff=lfs merge=lfs -text
-FordA/FordA_TRAIN.ts filter=lfs diff=lfs merge=lfs -text
-InlineSkate/InlineSkate_TEST.ts filter=lfs diff=lfs merge=lfs -text
-UWaveGestureLibraryZ/UWaveGestureLibraryZ_TEST.ts filter=lfs diff=lfs merge=lfs -text
-Yoga/Yoga_TEST.ts filter=lfs diff=lfs merge=lfs -text
-NonInvasiveFetalECGThorax1/NonInvasiveFetalECGThorax1_TRAIN.ts filter=lfs diff=lfs merge=lfs -text
-NonInvasiveFetalECGThorax1/NonInvasiveFetalECGThorax1_TEST.ts filter=lfs diff=lfs merge=lfs -text
-UWaveGestureLibraryAll/UWaveGestureLibraryAll_TEST.ts filter=lfs diff=lfs merge=lfs -text
-Heartbeat/Heartbeat_TEST.ts filter=lfs diff=lfs merge=lfs -text
-Heartbeat/Heartbeat_TRAIN.ts filter=lfs diff=lfs merge=lfs -text
-PhonemeSpectra/PhonemeSpectra_TRAIN.ts filter=lfs diff=lfs merge=lfs -text
-PhonemeSpectra/PhonemeSpectra_TEST.ts filter=lfs diff=lfs merge=lfs -text
-HandOutlines/HandOutlines_TEST.ts filter=lfs diff=lfs merge=lfs -text
-HandOutlines/HandOutlines_TRAIN.ts filter=lfs diff=lfs merge=lfs -text
-MotorImagery/MotorImagery_TEST.ts 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
+EigenWorms/EigenWorms_TRAIN.ts filter=lfs diff=lfs merge=lfs -text
+EigenWorms/EigenWorms_TEST.ts filter=lfs diff=lfs merge=lfs -text
+MixedShapesSmallTrain/MixedShapesSmallTrain_TEST.ts filter=lfs diff=lfs merge=lfs -text
+FordB/FordB_TRAIN.ts filter=lfs diff=lfs merge=lfs -text
+SpokenArabicDigits/SpokenArabicDigits_TRAIN.ts filter=lfs diff=lfs merge=lfs -text
+MixedShapesRegularTrain/MixedShapesRegularTrain_TEST.ts filter=lfs diff=lfs merge=lfs -text
+FaceDetection/FaceDetection_TEST.ts filter=lfs diff=lfs merge=lfs -text
+PEMS-SF/PEMS-SF_TEST.ts filter=lfs diff=lfs merge=lfs -text
+InsectWingbeat/InsectWingbeat_TEST.ts filter=lfs diff=lfs merge=lfs -text
+InsectWingbeat/InsectWingbeat_TRAIN.ts filter=lfs diff=lfs merge=lfs -text
+UWaveGestureLibraryX/UWaveGestureLibraryX_TEST.ts filter=lfs diff=lfs merge=lfs -text
+NonInvasiveFetalECGThorax2/NonInvasiveFetalECGThorax2_TRAIN.ts filter=lfs diff=lfs merge=lfs -text
+DuckDuckGeese/DuckDuckGeese_TRAIN.ts filter=lfs diff=lfs merge=lfs -text
+StarLightCurves/StarLightCurves_TRAIN.ts filter=lfs diff=lfs merge=lfs -text
+NonInvasiveFetalECGThorax2/NonInvasiveFetalECGThorax2_TEST.ts filter=lfs diff=lfs merge=lfs -text
+UWaveGestureLibraryY/UWaveGestureLibraryY_TEST.ts filter=lfs diff=lfs merge=lfs -text
+Phoneme/Phoneme_TEST.ts filter=lfs diff=lfs merge=lfs -text
+CinCECGTorso/CinCECGTorso_TEST.ts filter=lfs diff=lfs merge=lfs -text
+PEMS-SF/PEMS-SF_TRAIN.ts filter=lfs diff=lfs merge=lfs -text
+DuckDuckGeese/DuckDuckGeese_TEST.ts filter=lfs diff=lfs merge=lfs -text
+MotorImagery/MotorImagery_TRAIN.ts filter=lfs diff=lfs merge=lfs -text
+StarLightCurves/StarLightCurves_TEST.ts filter=lfs diff=lfs merge=lfs -text
+FaceDetection/FaceDetection_TRAIN.ts filter=lfs diff=lfs merge=lfs -text

Adiac/Adiac_TEST.ts ADDED Viewed

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Adiac/Adiac_TRAIN.ts ADDED Viewed

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AllGestureWiimoteY/AllGestureWiimoteY_TEST.ts ADDED Viewed

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AllGestureWiimoteY/AllGestureWiimoteY_TRAIN.ts ADDED Viewed

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BeetleFly/BeetleFly_TEST.ts ADDED Viewed

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BeetleFly/BeetleFly_TRAIN.ts ADDED Viewed

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CBF/CBF_TEST.ts ADDED Viewed

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CBF/CBF_TRAIN.ts ADDED Viewed

	@@ -0,0 +1,50 @@

+#Problems:
+#
+#Cylinder-Bell-Funnel simulated data. It was described in one of
+#Eamonns papers I think. SAX maybe?
+#
+#Introduced in Saito 1994
+#
+#CBF data set (Cylinder, Bell and Funnel) Naoki Saito. Local Feature
+#Extraction and Its Applications Using a Library of Bases. PhD
+#thesis, Department of Mathematics, Yale University, 1994.
+#
+#https://books.google.co.uk/books?id=tK7UCgAAQBAJ&pg=PA159&lpg=PA159&dq=Cylinder+bell+funnel+data+set&source=bl&ots=xrZ8sjldml&sig=bp2XgT90IQcsKR8S2bmpxvHCipk&hl=en&sa=X&ved=0ahUKEwiH4p73x7nMAhXrDsAKHXV0BKUQ6AEITzAM#v=onepage&q=Cylinder%20bell%20funnel%20data%20set&f=false
+@problemName CBF
+@timeStamps false
+@missing false
+@univariate true
+@equalLength true
+@seriesLength 128
+@classLabel true 1 2 3
+@data
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Chinatown/Chinatown_TEST.ts ADDED Viewed

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+## PedestrianCountingSystem dataset
+#
+#The City of Melbourne, Australia has developed an automated pedestrian counting system to better understand pedestrian activity within the municipality, such as how people use different city locations at different time of the day. The data analysis can facility decision making and urban planning for the future.
+#
+#We extract data of 10 locations for the whole year 2017. We make two datasets from these data.
+#
+### MelbournePedestrian (not this file) and Chinatown
+#
+#Data are pedestrian count in Chinatown-Swanston St (North for 12
+#months of the year 2017. Classes are based on whether data are from
+#a normal day or a weekend day.
+#
+#- Class 1: Weekend
+#- Class 2: Weekday
+#
+#Train size: 20
+#
+#Test size: 343
+#
+#Missing value: No
+#
+#Number of classses: 2
+#
+#Time series length: 24
+#
+#There is nothing to infer from the order of examples in the train and test set.
+#
+#Data source: City of Melbourne (see [1]). Data edited by Hoang Anh Dau.
+#
+#[1] http://www.pedestrian.melbourne.vic.gov.au/#date=11-06-2018&time=4
+@problemName Chinatown
+@timeStamps false
+@missing false
+@univariate true
+@equalLength true
+@seriesLength 24
+@classLabel true 1 2
+@data
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Chinatown/Chinatown_TRAIN.ts ADDED Viewed

	@@ -0,0 +1,58 @@

+## PedestrianCountingSystem dataset
+#
+#The City of Melbourne, Australia has developed an automated pedestrian counting system to better understand pedestrian activity within the municipality, such as how people use different city locations at different time of the day. The data analysis can facility decision making and urban planning for the future.
+#
+#We extract data of 10 locations for the whole year 2017. We make two datasets from these data.
+#
+### MelbournePedestrian (not this file) and Chinatown
+#
+#Data are pedestrian count in Chinatown-Swanston St (North for 12
+#months of the year 2017. Classes are based on whether data are from
+#a normal day or a weekend day.
+#
+#- Class 1: Weekend
+#- Class 2: Weekday
+#
+#Train size: 20
+#
+#Test size: 343
+#
+#Missing value: No
+#
+#Number of classses: 2
+#
+#Time series length: 24
+#
+#There is nothing to infer from the order of examples in the train and test set.
+#
+#Data source: City of Melbourne (see [1]). Data edited by Hoang Anh Dau.
+#
+#[1] http://www.pedestrian.melbourne.vic.gov.au/#date=11-06-2018&time=4
+@problemName Chinatown
+@timeStamps false
+@missing false
+@univariate true
+@equalLength true
+@seriesLength 24
+@classLabel true 1 2
+@data
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CinCECGTorso/CinCECGTorso_TEST.ts ADDED Viewed

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Computers/Computers_TRAIN.ts ADDED Viewed

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DuckDuckGeese/DuckDuckGeese_TEST.ts ADDED Viewed

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+#The data was derived from twelve monthly electrical power demand
+#time series from Italy and first used in the paper "Intelligent
+#Icons: Integrating Lite-Weight Data Mining and Visualization into
+#GUI Operating Systems". The classification task is to distinguish
+#days from Oct to March (inclusive) from April to September.
+@problemName ItalyPowerDemand
+@timeStamps false
+@missing false
+@univariate true
+@equalLength true
+@seriesLength 24
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