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Datasets:
williamgilpin
/
dysts

Tasks:
Time Series Forecasting
Size:
1M<n<10M
ArXiv:
Tags:
time
multivariate
forecasting
univariate-time-series-forecasting
multivariate-time-series-forecasting
License:
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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 2 new columns ({' x4', ' x3'})

This happened while the csv dataset builder was generating data using

hf://datasets/williamgilpin/dysts/coarse/ArnoldWeb_coarse.csv (at revision 5d305abb3c9010e36ca6a3e6c967b72932f6cca9)

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 1870, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 622, 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 2292, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2240, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              time: double
              x0: double
               x1: double
               x2: double
               x3: double
               x4: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 905
              to
              {'time': Value(dtype='float64', id=None), 'x0': Value(dtype='float64', id=None), ' x1': Value(dtype='float64', id=None), ' x2': 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 1417, 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 1049, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 924, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1000, 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 1741, 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 1872, 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 2 new columns ({' x4', ' x3'})
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/williamgilpin/dysts/coarse/ArnoldWeb_coarse.csv (at revision 5d305abb3c9010e36ca6a3e6c967b72932f6cca9)
              
              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.

time
float64
x0
float64
x1
float64
x2
float64
0
-0.784502
-0.628877
-0.176203
1
-0.708158
-0.681436
-0.189307
2
-0.628996
-0.725622
-0.201585
3
-0.547893
-0.761401
-0.213058
4
-0.465703
-0.788828
-0.223747
5
-0.383247
-0.808037
-0.233671
6
-0.301308
-0.819242
-0.242849
7
-0.220621
-0.822723
-0.251298
8
-0.141869
-0.818824
-0.259038
9
-0.065683
-0.807942
-0.26609
10
0.007368
-0.790522
-0.272476
11
0.076775
-0.767048
-0.278218
12
0.14209
-0.738035
-0.28334
13
0.20293
-0.704025
-0.287866
14
0.258976
-0.665576
-0.291821
15
0.309968
-0.623258
-0.295228
16
0.355711
-0.577646
-0.298109
17
0.396067
-0.529313
-0.300486
18
0.430956
-0.478826
-0.302379
19
0.460351
-0.426742
-0.303804
20
0.484279
-0.373599
-0.304778
21
0.502812
-0.319917
-0.305315
22
0.516068
-0.266191
-0.305428
23
0.524204
-0.212892
-0.305127
24
0.527416
-0.160458
-0.304423
25
0.525931
-0.109296
-0.303324
26
0.520005
-0.059781
-0.301839
27
0.509919
-0.012251
-0.299974
28
0.495973
0.03299
-0.297738
29
0.478486
0.075675
-0.295138
30
0.45779
0.115575
-0.29218
31
0.434223
0.152494
-0.288872
32
0.408132
0.186276
-0.285219
33
0.379866
0.216796
-0.281229
34
0.349772
0.243967
-0.276908
35
0.318193
0.267732
-0.272262
36
0.285468
0.288065
-0.267296
37
0.251926
0.304973
-0.262014
38
0.217885
0.318486
-0.256422
39
0.183649
0.328665
-0.250522
40
0.14951
0.335591
-0.244318
41
0.115741
0.339369
-0.237812
42
0.082599
0.340123
-0.231006
43
0.050323
0.337995
-0.2239
44
0.019131
0.333142
-0.216495
45
-0.010778
0.325735
-0.208789
46
-0.039225
0.315956
-0.200783
47
-0.066052
0.303997
-0.192475
48
-0.091122
0.290055
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49
-0.114319
0.274334
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50
-0.135545
0.257042
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51
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0.238387
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52
-0.171802
0.218578
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53
-0.186737
0.197821
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54
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0.17632
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55
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0.154276
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56
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0.131882
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57
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0.109325
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58
-0.229159
0.086784
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59
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0.064431
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60
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0.042427
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61
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0.020923
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62
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0.00006
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63
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64
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65
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0.012762
66
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0.027585
67
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0.042812
68
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0.058452
69
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0.07451
70
-0.147887
-0.130558
0.090996
71
-0.133405
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0.107916
72
-0.118313
-0.150361
0.125279
73
-0.102736
-0.158103
0.143091
74
-0.0868
-0.164392
0.16136
75
-0.070625
-0.169229
0.180092
76
-0.054331
-0.172625
0.199294
77
-0.038033
-0.174597
0.218972
78
-0.021841
-0.175174
0.239132
79
-0.005863
-0.174388
0.259779
80
0.009802
-0.172281
0.280917
81
0.025056
-0.168901
0.302551
82
0.039808
-0.164299
0.324682
83
0.053974
-0.158534
0.347313
84
0.067474
-0.151668
0.370444
85
0.080235
-0.143769
0.394076
86
0.092189
-0.134907
0.418206
87
0.103274
-0.125155
0.442832
88
0.113436
-0.114589
0.467948
89
0.122625
-0.103289
0.493548
90
0.130797
-0.091334
0.519624
91
0.137914
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0.546166
92
0.143945
-0.06579
0.57316
93
0.148863
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0.600593
94
0.152646
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0.628446
95
0.15528
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0.6567
96
0.156753
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0.685334
97
0.157061
0.00369
0.71432
98
0.156203
0.017874
0.743633
99
0.154183
0.031956
0.773242
End of preview.

Chaotic Time Series Dataset

Multivariate time series from chaotic dynamical systems.

  • Each multivariate time series is a drawn from one chaotic dynamical system over an extended duration, making this dataset suitable for long-horizon forecasting tasks.

  • There are 4 million total multivariate observations, grouped into 135 systems and three granularities

  • The subdirectories coarse, medium, and fine each contain 135 .csv files, each of which contains a single multivariate time series of length 10,000

  • The number of channels varies depending on the specific dynamical system.

  • The time series are stationary due to the ergodic property of chaotic systems.

Reference

For more information, or if using this code for published work, please cite the accompanying papers.

William Gilpin. "Chaos as an interpretable benchmark for forecasting and data-driven modelling" Advances in Neural Information Processing Systems (NeurIPS) 2021 https://arxiv.org/abs/2110.05266

William Gilpin. "Model scale versus domain knowledge in statistical forecasting of chaotic systems" Physical Review Research 2023 https://arxiv.org/abs/2303.08011

Code

For executable code, or to simulate new trajectories, please see the dysts repository on GitHub

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