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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 320 new columns ({'86', '189', '273', '120', '157', '285', '219', '296', '76', '230', '48', '79', '130', '116', '222', '227', '83', '124', '268', '309', '14', '109', '149', '214', '201', '220', '72', '103', '232', '188', '293', '142', '36', '198', '183', '140', '205', '54', '311', '53', '71', '226', '22', '192', '216', '92', '202', '294', '292', '217', '228', '88', '131', '123', '61', '238', '160', '127', '290', '295', '154', '251', '49', '143', '73', '91', '118', '90', '259', '144', '277', '190', '200', '82', '147', '141', '233', '179', '196', '41', '21', '246', '297', '30', '298', '101', '15', '150', '193', '319', '204', '299', '314', '70', '191', '213', '282', '146', '2', '210', '145', '275', '9', '235', '317', '224', '125', '270', '110', '256', '176', '208', '159', '312', '38', '284', '108', '306', '80', '42', '35', '107', '119', '281', '152', '237', '240', '249', '280', '13', '163', '313', '167', '55', '158', '26', '303', '96', '51', '148', '199', '67', '62', '153', '18', '137', '180', '6', '206', '74', '278', '162', '212', '318', '134', '77', '5', '47', '258', '45', '102', '271', '93', '305', '31', '8', '156', '264', '287', '111', '265', '117', '173', '168', '310', '28', '43', '185', '20', '97', '195', '104', '241', '56', '29', '63', '252', '308', '218', '40', '85', '236', '69', '203', '68', '121', '269', '247', '187', '7', '113', '16', '4', '46', '114', '65', '263', '115', '274', '307', '288', '37', '105', '182', '262', '289', '33', '132', '12', '139', '129', '59', '175', '239', '66', '209', '316', '174', '44', '87', '19', '89', '165', '24', '60', '164', '248', '1', '64', '255', '138', '57', '184', '276', '197', '267', '50', '178', '186', '0', '242', '221', '231', '225', '94', '58', '257', '99', '151', '211', '261', '34', '84', '253', '177', '172', '23', '300', '181', '207', '315', '95', '286', '169', '128', '27', '25', '112', '254', '133', '75', '135', '266', '126', '223', '3', '10', '171', '106', '161', '100', '32', '98', '304', '302', '166', '155', '272', '136', '229', '215', '245', '260', '122', '78', '244', '279', '243', '170', '81', '234', '301', '39', '283', '250', '17', '194', '52', '11', '291'}) and 6 missing columns ({'LUFL', 'MULL', 'MUFL', 'LULL', 'HULL', 'HUFL'}).
This happened while the csv dataset builder was generating data using
zip://all_six_datasets/electricity/electricity.csv::/tmp/hf-datasets-cache/medium/datasets/44982504243830-config-parquet-and-info-ym0v0my-Time_series_datas-52f5ac86/downloads/fbcaf11fb0831e8597a7febc4c0298e76ca846b5b06be6818a4538aaf759b73d
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
date: string
0: double
1: double
2: double
3: double
4: double
5: double
6: double
7: double
8: double
9: double
10: double
11: double
12: double
13: double
14: double
15: double
16: double
17: double
18: double
19: double
20: double
21: double
22: double
23: double
24: double
25: double
26: double
27: double
28: double
29: double
30: double
31: double
32: double
33: double
34: double
35: double
36: double
37: double
38: double
39: double
40: double
41: double
42: double
43: double
44: double
45: double
46: double
47: double
48: double
49: double
50: double
51: double
52: double
53: double
54: double
55: double
56: double
57: double
58: double
59: double
60: double
61: double
62: double
63: double
64: double
65: double
66: double
67: double
68: double
69: double
70: double
71: double
72: double
73: double
74: double
75: double
76: double
77: double
78: double
79: double
80: double
81: double
82: double
83: double
84: double
85: double
86: double
87: double
88: double
89: double
90: double
91: double
92: double
93: double
94: double
95: double
96: double
97: double
98: double
99: double
100: double
101: double
102: double
103: double
104: double
105: double
106: double
107: double
108: double
109: double
110: double
111: double
112: double
113: double
114: double
115: double
116: double
117: double
118: double
119: double
120: double
121: double
122: double
123: double
124: double
125: double
126: double
127: double
128: double
129: double
130: double
131: double
132: double
1
...
uble
205: double
206: double
207: double
208: double
209: double
210: double
211: double
212: double
213: double
214: double
215: double
216: double
217: double
218: double
219: double
220: double
221: double
222: double
223: double
224: double
225: double
226: double
227: double
228: double
229: double
230: double
231: double
232: double
233: double
234: double
235: double
236: double
237: double
238: double
239: double
240: double
241: double
242: double
243: double
244: double
245: double
246: double
247: double
248: double
249: double
250: double
251: double
252: double
253: double
254: double
255: double
256: double
257: double
258: double
259: double
260: double
261: double
262: double
263: double
264: double
265: double
266: double
267: double
268: double
269: double
270: double
271: double
272: double
273: double
274: double
275: double
276: double
277: double
278: double
279: double
280: double
281: double
282: double
283: double
284: double
285: double
286: double
287: double
288: double
289: double
290: double
291: double
292: double
293: double
294: double
295: double
296: double
297: double
298: double
299: double
300: double
301: double
302: double
303: double
304: double
305: double
306: double
307: double
308: double
309: double
310: double
311: double
312: double
313: double
314: double
315: double
316: double
317: double
318: double
319: double
OT: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 34496
to
{'date': Value(dtype='string', id=None), 'HUFL': Value(dtype='float64', id=None), 'HULL': Value(dtype='float64', id=None), 'MUFL': Value(dtype='float64', id=None), 'MULL': Value(dtype='float64', id=None), 'LUFL': Value(dtype='float64', id=None), 'LULL': Value(dtype='float64', id=None), 'OT': Value(dtype='float64', id=None)}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1321, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 935, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 320 new columns ({'86', '189', '273', '120', '157', '285', '219', '296', '76', '230', '48', '79', '130', '116', '222', '227', '83', '124', '268', '309', '14', '109', '149', '214', '201', '220', '72', '103', '232', '188', '293', '142', '36', '198', '183', '140', '205', '54', '311', '53', '71', '226', '22', '192', '216', '92', '202', '294', '292', '217', '228', '88', '131', '123', '61', '238', '160', '127', '290', '295', '154', '251', '49', '143', '73', '91', '118', '90', '259', '144', '277', '190', '200', '82', '147', '141', '233', '179', '196', '41', '21', '246', '297', '30', '298', '101', '15', '150', '193', '319', '204', '299', '314', '70', '191', '213', '282', '146', '2', '210', '145', '275', '9', '235', '317', '224', '125', '270', '110', '256', '176', '208', '159', '312', '38', '284', '108', '306', '80', '42', '35', '107', '119', '281', '152', '237', '240', '249', '280', '13', '163', '313', '167', '55', '158', '26', '303', '96', '51', '148', '199', '67', '62', '153', '18', '137', '180', '6', '206', '74', '278', '162', '212', '318', '134', '77', '5', '47', '258', '45', '102', '271', '93', '305', '31', '8', '156', '264', '287', '111', '265', '117', '173', '168', '310', '28', '43', '185', '20', '97', '195', '104', '241', '56', '29', '63', '252', '308', '218', '40', '85', '236', '69', '203', '68', '121', '269', '247', '187', '7', '113', '16', '4', '46', '114', '65', '263', '115', '274', '307', '288', '37', '105', '182', '262', '289', '33', '132', '12', '139', '129', '59', '175', '239', '66', '209', '316', '174', '44', '87', '19', '89', '165', '24', '60', '164', '248', '1', '64', '255', '138', '57', '184', '276', '197', '267', '50', '178', '186', '0', '242', '221', '231', '225', '94', '58', '257', '99', '151', '211', '261', '34', '84', '253', '177', '172', '23', '300', '181', '207', '315', '95', '286', '169', '128', '27', '25', '112', '254', '133', '75', '135', '266', '126', '223', '3', '10', '171', '106', '161', '100', '32', '98', '304', '302', '166', '155', '272', '136', '229', '215', '245', '260', '122', '78', '244', '279', '243', '170', '81', '234', '301', '39', '283', '250', '17', '194', '52', '11', '291'}) and 6 missing columns ({'LUFL', 'MULL', 'MUFL', 'LULL', 'HULL', 'HUFL'}).
This happened while the csv dataset builder was generating data using
zip://all_six_datasets/electricity/electricity.csv::/tmp/hf-datasets-cache/medium/datasets/44982504243830-config-parquet-and-info-ym0v0my-Time_series_datas-52f5ac86/downloads/fbcaf11fb0831e8597a7febc4c0298e76ca846b5b06be6818a4538aaf759b73d
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
date
string | HUFL
float64 | HULL
float64 | MUFL
float64 | MULL
float64 | LUFL
float64 | LULL
float64 | OT
float64 |
|---|---|---|---|---|---|---|---|
2016-07-01 00:00:00
| 5.827
| 2.009
| 1.599
| 0.462
| 4.203
| 1.34
| 30.531
|
2016-07-01 01:00:00
| 5.693
| 2.076
| 1.492
| 0.426
| 4.142
| 1.371
| 27.787001
|
2016-07-01 02:00:00
| 5.157
| 1.741
| 1.279
| 0.355
| 3.777
| 1.218
| 27.787001
|
2016-07-01 03:00:00
| 5.09
| 1.942
| 1.279
| 0.391
| 3.807
| 1.279
| 25.044001
|
2016-07-01 04:00:00
| 5.358
| 1.942
| 1.492
| 0.462
| 3.868
| 1.279
| 21.948
|
2016-07-01 05:00:00
| 5.626
| 2.143
| 1.528
| 0.533
| 4.051
| 1.371
| 21.174
|
2016-07-01 06:00:00
| 7.167
| 2.947
| 2.132
| 0.782
| 5.026
| 1.858
| 22.792
|
2016-07-01 07:00:00
| 7.435
| 3.282
| 2.31
| 1.031
| 5.087
| 2.224
| 23.143999
|
2016-07-01 08:00:00
| 5.559
| 3.014
| 2.452
| 1.173
| 2.955
| 1.432
| 21.667
|
2016-07-01 09:00:00
| 4.555
| 2.545
| 1.919
| 0.817
| 2.68
| 1.371
| 17.445999
|
2016-07-01 10:00:00
| 4.957
| 2.545
| 1.99
| 0.853
| 2.955
| 1.492
| 19.979
|
2016-07-01 11:00:00
| 5.76
| 2.545
| 2.203
| 0.853
| 3.442
| 1.492
| 20.118999
|
2016-07-01 12:00:00
| 4.689
| 2.545
| 1.812
| 0.853
| 2.833
| 1.523
| 19.205
|
2016-07-01 13:00:00
| 4.689
| 2.679
| 1.777
| 1.244
| 3.107
| 1.614
| 18.572001
|
2016-07-01 14:00:00
| 5.09
| 2.947
| 2.452
| 1.35
| 2.559
| 1.432
| 19.556
|
2016-07-01 15:00:00
| 5.09
| 3.148
| 2.487
| 1.35
| 2.589
| 1.523
| 17.305
|
2016-07-01 16:00:00
| 4.22
| 2.411
| 1.706
| 0.782
| 2.619
| 1.492
| 19.486
|
2016-07-01 17:00:00
| 4.756
| 2.344
| 1.635
| 0.711
| 3.076
| 1.492
| 19.134001
|
2016-07-01 18:00:00
| 5.626
| 2.88
| 2.523
| 1.208
| 3.076
| 1.492
| 20.681999
|
2016-07-01 19:00:00
| 5.492
| 3.014
| 2.452
| 1.208
| 3.015
| 1.553
| 18.712
|
2016-07-01 20:00:00
| 5.358
| 3.014
| 2.452
| 1.208
| 2.863
| 1.523
| 17.868
|
2016-07-01 21:00:00
| 5.09
| 2.947
| 2.381
| 1.208
| 2.68
| 1.523
| 18.009001
|
2016-07-01 22:00:00
| 4.823
| 2.947
| 2.203
| 1.173
| 2.619
| 1.523
| 18.009001
|
2016-07-01 23:00:00
| 4.622
| 2.88
| 2.132
| 1.137
| 2.467
| 1.492
| 19.768
|
2016-07-02 00:00:00
| 5.224
| 3.081
| 2.701
| 1.315
| 2.437
| 1.523
| 21.104
|
2016-07-02 01:00:00
| 5.157
| 3.014
| 2.878
| 1.35
| 2.345
| 1.432
| 19.697001
|
2016-07-02 02:00:00
| 5.157
| 3.148
| 2.878
| 1.492
| 2.284
| 1.432
| 20.049
|
2016-07-02 03:00:00
| 5.157
| 3.081
| 2.914
| 1.492
| 2.193
| 1.401
| 20.752001
|
2016-07-02 04:00:00
| 4.555
| 3.081
| 2.452
| 1.492
| 2.193
| 1.401
| 21.385
|
2016-07-02 05:00:00
| 5.425
| 3.282
| 3.092
| 1.706
| 2.437
| 1.462
| 22.23
|
2016-07-02 06:00:00
| 5.492
| 3.282
| 2.523
| 1.492
| 2.985
| 1.462
| 20.26
|
2016-07-02 07:00:00
| 5.626
| 3.215
| 2.487
| 1.492
| 3.076
| 1.523
| 21.104
|
2016-07-02 08:00:00
| 5.559
| 3.282
| 2.594
| 1.67
| 2.924
| 1.523
| 20.612
|
2016-07-02 09:00:00
| 5.224
| 3.215
| 2.559
| 1.564
| 2.68
| 1.462
| 18.361
|
2016-07-02 10:00:00
| 9.913
| 4.957
| 6.645
| 3.305
| 3.046
| 1.553
| 20.962999
|
2016-07-02 11:00:00
| 11.788
| 5.425
| 8.173
| 2.523
| 3.686
| 1.675
| 19.416
|
2016-07-02 12:00:00
| 9.645
| 4.957
| 6.752
| 2.132
| 3.107
| 1.828
| 20.823
|
2016-07-02 13:00:00
| 10.382
| 5.76
| 7.462
| 2.559
| 2.985
| 1.767
| 20.190001
|
2016-07-02 14:00:00
| 8.774
| 4.689
| 6.112
| 2.025
| 2.894
| 1.919
| 21.315001
|
2016-07-02 15:00:00
| 10.449
| 5.157
| 6.965
| 2.452
| 2.772
| 1.736
| 22.018999
|
2016-07-02 16:00:00
| 9.846
| 4.823
| 7.036
| 2.665
| 2.894
| 1.767
| 20.681999
|
2016-07-02 17:00:00
| 9.913
| 4.823
| 6.894
| 2.416
| 3.229
| 1.736
| 25.466
|
2016-07-02 18:00:00
| 10.65
| 4.689
| 6.929
| 2.452
| 3.381
| 1.797
| 25.888
|
2016-07-02 19:00:00
| 10.114
| 4.354
| 6.645
| 1.812
| 3.107
| 1.736
| 27.857
|
2016-07-02 20:00:00
| 9.98
| 4.153
| 6.574
| 1.954
| 3.411
| 1.767
| 27.295
|
2016-07-02 21:00:00
| 9.31
| 4.22
| 6.005
| 2.132
| 3.229
| 1.858
| 22.23
|
2016-07-02 22:00:00
| 9.444
| 4.622
| 6.965
| 2.168
| 2.955
| 1.858
| 21.948
|
2016-07-02 23:00:00
| 9.444
| 4.287
| 6.823
| 2.559
| 2.589
| 1.736
| 27.295
|
2016-07-03 00:00:00
| 10.382
| 5.425
| 7.604
| 2.31
| 2.955
| 1.675
| 29.334999
|
2016-07-03 01:00:00
| 9.779
| 5.224
| 6.716
| 2.843
| 2.65
| 1.675
| 26.028
|
2016-07-03 02:00:00
| 10.382
| 4.689
| 7.32
| 2.203
| 2.985
| 1.858
| 24.34
|
2016-07-03 03:00:00
| 9.779
| 4.153
| 6.823
| 1.99
| 2.528
| 1.675
| 26.450001
|
2016-07-03 04:00:00
| 10.717
| 4.756
| 7.356
| 2.807
| 2.65
| 1.797
| 25.958
|
2016-07-03 05:00:00
| 10.315
| 4.689
| 7.391
| 2.452
| 2.924
| 1.858
| 24.059
|
2016-07-03 06:00:00
| 12.592
| 5.224
| 8.671
| 2.203
| 3.716
| 1.949
| 25.325001
|
2016-07-03 07:00:00
| 11.119
| 4.622
| 7.889
| 2.843
| 3.625
| 1.919
| 23.636999
|
2016-07-03 08:00:00
| 10.65
| 4.421
| 7.036
| 2.025
| 3.594
| 1.919
| 26.379999
|
2016-07-03 09:00:00
| 10.047
| 4.22
| 6.432
| 1.67
| 3.686
| 1.949
| 27.365
|
2016-07-03 10:00:00
| 11.721
| 5.09
| 7.889
| 2.559
| 3.564
| 1.858
| 28.068001
|
2016-07-03 11:00:00
| 12.123
| 5.358
| 8.066
| 2.487
| 4.082
| 1.919
| 29.475
|
2016-07-03 12:00:00
| 9.98
| 5.023
| 6.858
| 2.559
| 3.29
| 1.858
| 26.802
|
2016-07-03 13:00:00
| 9.243
| 4.957
| 6.29
| 2.63
| 3.137
| 1.888
| 29.968
|
2016-07-03 14:00:00
| 10.181
| 5.425
| 7.178
| 3.02
| 3.076
| 1.888
| 30.389999
|
2016-07-03 15:00:00
| 9.645
| 5.425
| 7.107
| 2.665
| 3.015
| 1.828
| 31.164
|
2016-07-03 16:00:00
| 9.779
| 4.89
| 6.503
| 2.985
| 3.076
| 2.01
| 29.757
|
2016-07-03 17:00:00
| 11.119
| 5.157
| 7.32
| 2.914
| 3.807
| 1.98
| 32.289001
|
2016-07-03 18:00:00
| 11.052
| 4.957
| 7.391
| 2.523
| 3.686
| 1.98
| 31.938
|
2016-07-03 19:00:00
| 10.784
| 4.89
| 7.214
| 2.487
| 3.594
| 1.888
| 28.561001
|
2016-07-03 20:00:00
| 11.186
| 4.89
| 7.178
| 2.345
| 3.96
| 1.919
| 21.525999
|
2016-07-03 21:00:00
| 10.449
| 4.89
| 6.61
| 2.31
| 3.807
| 2.041
| 22.23
|
2016-07-03 22:00:00
| 9.578
| 5.76
| 6.787
| 3.127
| 3.259
| 1.888
| 19.416
|
2016-07-03 23:00:00
| 9.31
| 5.76
| 6.61
| 3.056
| 3.168
| 1.888
| 18.572001
|
2016-07-04 00:00:00
| 9.913
| 5.894
| 6.254
| 2.63
| 3.015
| 1.858
| 21.667
|
2016-07-04 01:00:00
| 8.975
| 4.957
| 6.29
| 2.665
| 2.863
| 1.828
| 25.535999
|
2016-07-04 02:00:00
| 8.64
| 4.823
| 6.148
| 2.594
| 2.924
| 1.828
| 27.857
|
2016-07-04 03:00:00
| 9.176
| 5.492
| 5.579
| 2.381
| 2.863
| 1.858
| 27.927999
|
2016-07-04 04:00:00
| 9.109
| 4.823
| 5.65
| 2.523
| 2.772
| 1.797
| 24.621
|
2016-07-04 05:00:00
| 9.846
| 5.559
| 5.97
| 2.949
| 3.107
| 1.888
| 23.848
|
2016-07-04 06:00:00
| 11.588
| 5.425
| 7.391
| 2.807
| 3.807
| 1.98
| 23.073999
|
2016-07-04 07:00:00
| 11.788
| 6.095
| 7.214
| 2.985
| 3.899
| 2.041
| 22.511
|
2016-07-04 08:00:00
| 10.583
| 5.961
| 7.143
| 2.914
| 3.655
| 2.071
| 21.667
|
2016-07-04 09:00:00
| 11.588
| 6.296
| 7.569
| 3.056
| 3.472
| 2.01
| 25.395
|
2016-07-04 10:00:00
| 11.922
| 6.229
| 7.711
| 3.056
| 3.746
| 1.949
| 25.184
|
2016-07-04 11:00:00
| 12.324
| 5.559
| 8.422
| 3.234
| 4.203
| 1.98
| 29.546
|
2016-07-04 12:00:00
| 10.382
| 5.894
| 6.858
| 2.63
| 3.564
| 1.949
| 29.475
|
2016-07-04 13:00:00
| 10.047
| 5.425
| 6.752
| 3.02
| 3.32
| 1.949
| 29.264
|
2016-07-04 14:00:00
| 10.516
| 6.028
| 7.107
| 3.376
| 3.137
| 1.919
| 30.952999
|
2016-07-04 15:00:00
| 10.717
| 6.095
| 6.787
| 3.02
| 3.168
| 2.01
| 31.726
|
2016-07-04 16:00:00
| 9.98
| 5.023
| 6.503
| 2.559
| 3.442
| 2.041
| 33.132999
|
2016-07-04 17:00:00
| 11.32
| 5.09
| 7.356
| 2.452
| 3.868
| 2.041
| 28.983
|
2016-07-04 18:00:00
| 11.387
| 4.957
| 7.356
| 2.452
| 4.295
| 2.193
| 28.983
|
2016-07-04 19:00:00
| 9.377
| 3.885
| 6.894
| 2.239
| 2.467
| 1.188
| 31.726
|
2016-07-04 20:00:00
| 10.114
| 4.086
| 7.143
| 2.239
| 2.955
| 1.462
| 25.184
|
2016-07-04 21:00:00
| 10.382
| 4.823
| 6.894
| 2.31
| 3.503
| 2.01
| 30.531
|
2016-07-04 22:00:00
| 9.645
| 4.89
| 6.61
| 1.919
| 3.259
| 1.919
| 27.646
|
2016-07-04 23:00:00
| 12.726
| 6.497
| 9.346
| 3.482
| 3.168
| 1.98
| 25.466
|
2016-07-05 00:00:00
| 11.989
| 5.626
| 8.777
| 2.949
| 3.198
| 1.98
| 25.958
|
2016-07-05 01:00:00
| 12.525
| 6.296
| 8.955
| 3.163
| 3.137
| 2.01
| 25.958
|
2016-07-05 02:00:00
| 12.324
| 6.296
| 8.813
| 3.376
| 2.985
| 1.919
| 26.028
|
2016-07-05 03:00:00
| 10.717
| 5.425
| 8.066
| 2.878
| 2.833
| 1.858
| 28.913
|
End of preview.
include six common Time-series-forcasting dataset
- ETTsmall
- ETTh1
- ETTh2
- ETTm1
- ETTm2
- traffic
- eletricity
- illness
- exchange_rate
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