Error code:   ConfigNamesError
Exception:    TypeError
Message:      list_() takes at least 1 positional argument (0 given)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                                   ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1207, in dataset_module_factory
                  raise e1 from None
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1182, in dataset_module_factory
                  ).get_module()
                    ^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 612, in get_module
                  dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data)
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/info.py", line 379, in from_dataset_card_data
                  dataset_info_yaml_dict.get("config_name", "default"): DatasetInfo._from_yaml_dict(
                                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/info.py", line 317, in _from_yaml_dict
                  yaml_data["features"] = Features._from_yaml_list(yaml_data["features"])
                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2138, in _from_yaml_list
                  return cls.from_dict(from_yaml_inner(yaml_data))
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2134, in from_yaml_inner
                  return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
                                ^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2123, in from_yaml_inner
                  Value(obj["dtype"])
                File "<string>", line 5, in __init__
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 552, in __post_init__
                  self.pa_type = string_to_arrow(self.dtype)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 156, in string_to_arrow
                  return pa.__dict__[datasets_dtype + "_"]()
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/types.pxi", line 4942, in pyarrow.lib.list_
              TypeError: list_() takes at least 1 positional argument (0 given)

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Wikimedia Pageview Time Series

Preprocessed time series dataset derived from Wikimedia pageview statistics. Contains fixed-length windows of Wikipedia article pageview counts at hourly and daily resolution, plus STL seasonal-trend decomposition components.

Dataset Summary

Subset	Series Count	Series Length	Size	Description
`wiki_hourly`	3,715,121	1025	2.5 GB	Hourly pageview counts
`wiki_daily`	1,990,244	1025	2.5 GB	Daily aggregated pageview counts
`wiki_stl_residual`	530,731	1025	2.0 GB	STL decomposition — residual component
`wiki_stl_seasonal`	371,512	1025	1.4 GB	STL decomposition — seasonal component
`wiki_stl_trend`	159,219	1025	587 MB	STL decomposition — trend component

Total: ~6.77M time series, ~9 GB

Schema

Each parquet file contains three columns:

Column	Type	Description
`series`	`fixed_size_list<float32>[1025]`	The time series values (1024 input steps + 1 target)
`source_id`	`uint8`	Numeric identifier for the data source/component
`meta`	`string`	Human-readable component name

Source IDs

`source_id`	`meta` value	Description
1	`wiki_hourly`	Raw hourly pageview counts
2	`wiki_daily`	Daily aggregated pageview counts
3	`wiki_stl_residual`	Residual after STL decomposition
4	`wiki_stl_seasonal`	Seasonal component from STL decomposition
5	`wiki_stl_trend`	Trend component from STL decomposition

Data Origin

Source: Wikimedia pageview complete dumps
Date range: December 2011 — October 2016
Filtering: Pages with fewer than 10 daily views are excluded
Processing pipeline:
1. Raw hourly .bz2 dumps downloaded from Wikimedia
2. Parsed and aggregated into weekly parquet files
3. Stitched into fixed-length windows of T=1025 time steps (1024 + 1)
4. STL seasonal-trend decomposition applied to extract trend, seasonal, and residual components
5. Daily aggregation computed from hourly data

Usage

from datasets import load_dataset

# Load a specific subset
ds = load_dataset("jeremycochoy/wikimedia-pageview-timeseries", "wiki_daily")

# Access a time series
series = ds["train"][0]["series"]  # list of 1025 floats

Or load directly with PyArrow:

import pyarrow.parquet as pq

table = pq.read_table("wiki_daily/wiki_daily_file000_00000.parquet")
df = table.to_pandas()
series = df["series"].iloc[0]  # numpy array of shape (1025,)

Data Characteristics

Patterns present: viral spikes, seasonal cycles (holidays, sports events, school calendars), slow decays, flat/stable pages, multi-language diversity (all Wikimedia projects)
Languages: All Wikimedia language editions included (English, German, French, Japanese, Russian, etc.)
Use cases: Time series foundation model pretraining, forecasting benchmarks, transfer learning

License

The underlying Wikimedia pageview data is released under CC0 1.0 (Public Domain). This preprocessed dataset inherits that license.

Downloads last month: 603

Total file size:

9.37 GB