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---
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license: apache-2.0
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dataset_info:
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features:
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- name: id
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dtype: int32
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- name: x
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dtype:
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array2_d:
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shape:
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- 24
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dtype: float32
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- name: station
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dtype:
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class_label:
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names:
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'0': Aotizhongxin
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'1': Changping
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'2': Dingling
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'3': Dongsi
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'4': Guanyuan
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'5': Gucheng
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'6': Huairou
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'7': Nongzhanguan
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'8': Shunyi
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'9': Tiantan
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'10': Wanliu
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'11': Wanshouxigong
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- name: year
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dtype:
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class_label:
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names:
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'0': '2013'
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'1': '2014'
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'2': '2015'
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'3': '2016'
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'4': '2017'
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- name: month
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dtype:
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class_label:
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names:
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'0': '1'
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'1': '2'
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'2': '3'
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'3': '4'
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'4': '5'
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'5': '6'
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'6': '7'
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'7': '8'
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'8': '9'
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'9': '10'
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'10': '11'
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'11': '12'
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- name: day
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dtype:
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class_label:
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names:
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'0': '1'
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'1': '2'
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'2': '3'
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'3': '4'
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'4': '5'
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'5': '6'
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'6': '7'
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'7': '8'
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'8': '9'
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'9': '10'
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'10': '11'
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'11': '12'
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'12': '13'
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'13': '14'
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'14': '15'
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'15': '16'
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'16': '17'
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'17': '18'
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'18': '19'
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'19': '20'
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'20': '21'
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'21': '22'
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'22': '23'
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'23': '24'
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'24': '25'
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'25': '26'
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'26': '27'
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'27': '28'
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'28': '29'
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'29': '30'
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'30': '31'
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- name: season
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dtype:
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class_label:
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names:
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'0': Summer
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'1': Autumn
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'2': Winter
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'3': Spring
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splits:
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- name: train
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num_bytes: 17082624
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num_examples: 12272
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- name: val
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num_bytes: 3660960
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num_examples: 2630
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- name: test
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num_bytes: 3660960
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num_examples: 2630
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download_size: 9462423
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dataset_size: 24404544
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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- split: val
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path: data/val-*
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- split: test
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path: data/test-*
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---
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---
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license: apache-2.0
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dataset_info:
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features:
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- name: id
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dtype: int32
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- name: x
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dtype:
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array2_d:
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shape:
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- 24
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- 13
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dtype: float32
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- name: station
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dtype:
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class_label:
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names:
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'0': Aotizhongxin
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'1': Changping
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'2': Dingling
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'3': Dongsi
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'4': Guanyuan
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| 23 |
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'5': Gucheng
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| 24 |
+
'6': Huairou
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| 25 |
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'7': Nongzhanguan
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| 26 |
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'8': Shunyi
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| 27 |
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'9': Tiantan
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'10': Wanliu
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'11': Wanshouxigong
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- name: year
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dtype:
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class_label:
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names:
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'0': '2013'
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'1': '2014'
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'2': '2015'
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'3': '2016'
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'4': '2017'
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- name: month
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dtype:
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class_label:
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names:
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'0': '1'
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'1': '2'
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'2': '3'
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'3': '4'
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'4': '5'
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'5': '6'
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'6': '7'
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'7': '8'
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| 51 |
+
'8': '9'
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| 52 |
+
'9': '10'
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| 53 |
+
'10': '11'
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| 54 |
+
'11': '12'
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| 55 |
+
- name: day
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| 56 |
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dtype:
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| 57 |
+
class_label:
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names:
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'0': '1'
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+
'1': '2'
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+
'2': '3'
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+
'3': '4'
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+
'4': '5'
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+
'5': '6'
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'6': '7'
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'7': '8'
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'8': '9'
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'9': '10'
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'10': '11'
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'11': '12'
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'12': '13'
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'13': '14'
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'14': '15'
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'15': '16'
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'16': '17'
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'17': '18'
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'18': '19'
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'19': '20'
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'20': '21'
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'21': '22'
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'22': '23'
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'23': '24'
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'24': '25'
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'25': '26'
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'26': '27'
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'27': '28'
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'28': '29'
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'29': '30'
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'30': '31'
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- name: season
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dtype:
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class_label:
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names:
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'0': Summer
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'1': Autumn
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'2': Winter
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'3': Spring
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splits:
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- name: train
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num_bytes: 17082624
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num_examples: 12272
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- name: val
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num_bytes: 3660960
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num_examples: 2630
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- name: test
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num_bytes: 3660960
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num_examples: 2630
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download_size: 9462423
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dataset_size: 24404544
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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- split: val
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path: data/val-*
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- split: test
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path: data/test-*
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---
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## MSD Beijing Multi-Site Air Quality Dataset Attribution
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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.
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- Original source:
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https://archive.ics.uci.edu/dataset/501/beijing+multi+site+air+quality+data
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```
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@misc{beijing_multi-site_air_quality_501,
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author = {Chen, Song},
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title = {{Beijing Multi-Site Air Quality}},
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year = {2017},
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howpublished = {UCI Machine Learning Repository},
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note = {{DOI}: https://doi.org/10.24432/C5RK5G}
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}
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```
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⚠ **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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