Datasets:

jeremycochoy
/

wikimedia-pageview-timeseries

license: cc0-1.0
task_categories:
  - time-series-forecasting
tags:
  - time-series
  - wikimedia
  - pageviews
  - wikipedia
  - stl-decomposition
  - pretraining
size_categories:
  - 1M-10M
pretty_name: Wikimedia Pageview Time Series
dataset_info:
  - config_name: wiki_daily
    features:
      - name: series
        dtype: list
        list:
          dtype: float32
          length: 1025
      - name: source_id
        dtype: uint8
      - name: meta
        dtype: string
    splits:
      - name: train
        num_examples: 1990244
  - config_name: wiki_hourly
    features:
      - name: series
        dtype: list
        list:
          dtype: float32
          length: 1025
      - name: source_id
        dtype: uint8
      - name: meta
        dtype: string
    splits:
      - name: train
        num_examples: 3715121
  - config_name: wiki_stl_residual
    features:
      - name: series
        dtype: list
        list:
          dtype: float32
          length: 1025
      - name: source_id
        dtype: uint8
      - name: meta
        dtype: string
    splits:
      - name: train
        num_examples: 530731
  - config_name: wiki_stl_seasonal
    features:
      - name: series
        dtype: list
        list:
          dtype: float32
          length: 1025
      - name: source_id
        dtype: uint8
      - name: meta
        dtype: string
    splits:
      - name: train
        num_examples: 371512
  - config_name: wiki_stl_trend
    features:
      - name: series
        dtype: list
        list:
          dtype: float32
          length: 1025
      - name: source_id
        dtype: uint8
      - name: meta
        dtype: string
    splits:
      - name: train
        num_examples: 159219
configs:
  - config_name: wiki_daily
    data_files: wiki_daily/*.parquet
  - config_name: wiki_hourly
    data_files: wiki_hourly/*.parquet
  - config_name: wiki_stl_residual
    data_files: wiki_stl_residual/*.parquet
  - config_name: wiki_stl_seasonal
    data_files: wiki_stl_seasonal/*.parquet
  - config_name: wiki_stl_trend
    data_files: wiki_stl_trend/*.parquet

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.