Add datasets.

Author: habibirani

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+<p> Sleep contains 153 whole-night sleeping Electroencephalography
+(EEG) recordings taken from physionet https://www.physionet.org/content/sleep-edfx/1.0.0/ and formatted into a classification problem in [1]. The data is collected
+from 82 healthy subjects. The 1-lead EEG signal is sampled at 100 Hz.</p>
+
+<p>The series were segmented into non overlapping sub series, each of which forms a case. Each case is labelled with one of the five sleeping patterns/stages: Wake (W), Nonrapid
+eye movement (N1, N2, N3) and Rapid Eye Movement (REM). The classes are not balanced and there are differences in the class distribution for the train and test. Train/test proportions in brackets after name.</p>
+
+<ol>
+<li>Wake (13.84% in train and 4.05% in test)
+<li>Non rapid eye movement type 1 (6.56% in train and 10.98% in test)
+<li>Non rapid eye movement type 2 (42.73% in train and 48.93% in test)
+<li>Non rapid eye movement type 3 (15.81% in train and 18.82% in test)
+<li>Rapid Eye Movement (21.06% in train and 18.82% in test)
+</ol>
+
+
+The data were split in [1] into 371,055 train cases, 107,730 validation and 90,315 test. We have added the validation set to the end of the train file to ease reproduction if a validation set is needed. For reference, a MiniRocket classifier gets accuracy of 92.7% on the default test data.</p>
+
+[1] Zhang et al. Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency, NeurIPS 2022.
+

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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
+
+Data are pedestrian count for 12 months of the year 2017. Classes correspond location of sensor placement.
+
+- Class 1: Bourke Street Mall (North)
+- Class 2: Southern Cross Station
+- Class 3: New Quay
+- Class 4: Flinders St Station Underpass
+- Class 5: QV Market-Elizabeth (West)
+- Class 6: Convention/Exhibition Centre
+- Class 7: Chinatown-Swanston St (North)
+- Class 8: Webb Bridge
+- Class 9: Tin Alley-Swanston St (West)
+- Class 10: Southbank
+
+Train size: 1200
+
+Test size: 2450
+
+Missing value: No
+
+Number of classses: 10
+
+Time series length: 24
+
+## 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: 345
+
+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