Search is not available for this dataset
id
int32 0
12.3k
| x
array 2D | station
class label 12
classes | year
class label 5
classes | month
class label 12
classes | day
class label 31
classes | season
class label 4
classes |
|---|---|---|---|---|---|---|
0
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| 1Changping
| 32016
| 89
| 2728
| 1Autumn
|
1
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| 0Aotizhongxin
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| 01
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| 0Aotizhongxin
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| 56
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| 3Dongsi
| 32016
| 56
| 2728
| 0Summer
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6
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| 11Wanshouxigong
| 22015
| 89
| 2829
| 1Autumn
|
7
|
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| 0Aotizhongxin
| 32016
| 34
| 2930
| 3Spring
|
8
|
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| 6Huairou
| 12014
| 34
| 1314
| 3Spring
|
9
|
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| 7Nongzhanguan
| 42017
| 01
| 1415
| 2Winter
|
10
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| 8Shunyi
| 22015
| 89
| 56
| 1Autumn
|
11
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| 7Nongzhanguan
| 22015
| 89
| 67
| 1Autumn
|
12
|
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| 1Changping
| 02013
| 89
| 89
| 1Autumn
|
13
|
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| 5Gucheng
| 32016
| 1011
| 12
| 1Autumn
|
14
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| 2Dingling
| 12014
| 56
| 2829
| 0Summer
|
15
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| 5Gucheng
| 12014
| 45
| 45
| 3Spring
|
16
|
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| 2Dingling
| 02013
| 23
| 1112
| 2Winter
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17
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| 10Wanliu
| 32016
| 56
| 2930
| 0Summer
|
18
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| 10Wanliu
| 32016
| 67
| 1920
| 0Summer
|
19
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| 1Changping
| 22015
| 45
| 1213
| 3Spring
|
20
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| 1Changping
| 02013
| 23
| 89
| 2Winter
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21
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| 6Huairou
| 22015
| 1112
| 89
| 2Winter
|
22
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| 10Wanliu
| 22015
| 45
| 2021
| 3Spring
|
23
|
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| 9Tiantan
| 12014
| 23
| 89
| 2Winter
|
24
|
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| 7Nongzhanguan
| 02013
| 89
| 89
| 1Autumn
|
25
|
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| 3Dongsi
| 12014
| 01
| 56
| 2Winter
|
26
|
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| 3Dongsi
| 32016
| 78
| 01
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28
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| 3Dongsi
| 12014
| 67
| 2021
| 0Summer
|
29
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| 11Wanshouxigong
| 12014
| 910
| 56
| 1Autumn
|
30
|
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| 4Guanyuan
| 12014
| 34
| 78
| 3Spring
|
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MSD Beijing Multi-Site Air Quality Dataset Attribution
The Multi-factor Sequential Disentanglement benchmark includes the Beijing Multi-Site Air Quality (BMS-AQ) dataset, a time series dataset that captures daily air quality and weather measurements across multiple monitoring stations in Beijing. For the benchmark, we preprocess this data into daily sequences of 24 hourly records, grouped by station and date. Each sequence is labeled with static attributes such as station, year, month, day, and season, resulting in a temporal dataset suitable for studying disentanglement in real-world time series.
Original source:
https://archive.ics.uci.edu/dataset/501/beijing+multi+site+air+quality+data@misc{beijing_multi-site_air_quality_501, author = {Chen, Song}, title = {{Beijing Multi-Site Air Quality}}, year = {2017}, howpublished = {UCI Machine Learning Repository}, note = {{DOI}: https://doi.org/10.24432/C5RK5G} }
⚠ Note: We process and redistribute this dataset solely for non-commercial research purposes. Please cite the above paper when using this dataset in your work.
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