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@@ -66,3 +66,11 @@ classification/UCR/FaceDetection/FaceDetection_TEST.ts filter=lfs diff=lfs merge
 classification/UCR/FaceDetection/FaceDetection_TRAIN.ts filter=lfs diff=lfs merge=lfs -text
 classification/UCR/FordA/FordA_TRAIN.ts filter=lfs diff=lfs merge=lfs -text
 classification/UCR/FordB/FordB_TRAIN.ts filter=lfs diff=lfs merge=lfs -text
+classification/UCR/HandOutlines/HandOutlines_TEST.ts filter=lfs diff=lfs merge=lfs -text
+classification/UCR/HandOutlines/HandOutlines_TRAIN.ts filter=lfs diff=lfs merge=lfs -text
+classification/UCR/Heartbeat/Heartbeat_TEST.ts filter=lfs diff=lfs merge=lfs -text
+classification/UCR/Heartbeat/Heartbeat_TRAIN.ts filter=lfs diff=lfs merge=lfs -text
+classification/UCR/InlineSkate/InlineSkate_TEST.ts filter=lfs diff=lfs merge=lfs -text
+classification/UCR/InsectWingbeat/InsectWingbeat_TEST.ts filter=lfs diff=lfs merge=lfs -text
+classification/UCR/InsectWingbeat/InsectWingbeat_TRAIN.ts filter=lfs diff=lfs merge=lfs -text
+classification/UCR/Mallat/Mallat_TEST.ts filter=lfs diff=lfs merge=lfs -text

classification/UCR/HandOutlines/HandOutlines_TEST.ts

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classification/UCR/InsectEPGSmallTrain/InsectEPGSmallTrain_TRAIN.ts

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+## InsectEPG dataset
+#
+#The electrical penetration graph or EPG is a system used by biologists to study the interaction of insects with plants. EPG data capture voltage changes of the electrical circuit that connects insects and their food source.  
+#
+#We make two datasets out of these data. The two datasets share a same test set and only differ in the number of training instances.
+#
+### InsectEPGRegularTrain
+#
+#Train size: 62
+#
+#Test size: 249
+#
+#Missing value: No
+#
+#Number of classes: 3
+#
+#Time series length: 601
+#
+### InsectEPGSmallTrain
+#
+#Train size: 17
+#
+#Test size: 249
+#
+#Missing value: No
+#
+#Number of classes: 3
+#
+#Time series length: 601
+#
+#There is nothing to infer from the order of examples in the train and test set.
+#
+#Original data created by Willett DS, George J, Willett NS Stelinski LL, Lapointe SL (see [1], [2]. Edited by Eamonn Keogh and Shaghayegh Gharghabi.
+#
+#[1] Willett DS, George J, Willett NS, Stelinski LL, Lapointe SL (2016) Machine Learning for Characterization of Insect Vector Feeding. PLOS Computational Biology 12(11): e1005158. 
+#
+#[2] https://datadryad.org//resource/doi:10.5061/dryad.4931c
+@problemName InsectEPGSmallTrain
+@timeStamps false
+@missing false
+@univariate true
+@equalLength true
+@seriesLength 601
+@classLabel true 1 2 3
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classification/UCR/InsectWingbeat/InsectWingbeat_TEST.ts

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classification/UCR/InsectWingbeat/InsectWingbeat_TRAIN.ts

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classification/UCR/ItalyPowerDemand/ItalyPowerDemand_TRAIN.ts

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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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classification/UCR/Mallat/Mallat_TEST.ts

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