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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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