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pretty_name: "Predictions \u2014 Google Community Mobility Reports \u2014 global daily"
license: other
tags:
- epi-eval
- predictions
- forecast-evaluation
- companion-of-global-mobility
configs:
- config_name: default
data_files:
- split: train
path: data/*.parquet
---
# Predictions for Google Community Mobility Reports — global daily
Community-submitted forecasts targeting [`EPI-Eval/global-mobility`](https://huggingface.co/datasets/EPI-Eval/global-mobility).
Each row is one quantile (or point) forecast for one target date — see the
schema below.
This repo accumulates accepted submissions from many forecasters. New
predictions arrive as community pull requests opened from the [EPI-Eval
dashboard](https://github.com/ChrisHarig/apart-forecasting-tool); a
maintainer reviews each PR before merging.
## Schema (v1)
| column | type | notes |
| --- | --- | --- |
| `target_date` | string (`YYYY-MM-DD`) | The date being forecast |
| `target_dataset` | string | Always `global-mobility` |
| `target_column` | string | Truth column being forecast (see below) |
| `submitter` | string | Forecaster name or HF username |
| `model_name` | string | Identifier for the model run |
| `description` | string | Free-form notes on the model |
| `quantile` | float (nullable) | In `[0, 1]`. `null` = point estimate |
| `value` | float | Forecast value (in the truth column's units) |
| `submitted_at` | string (ISO 8601) | UTC submission timestamp |
| _(pass-through dims)_ | string | Categorical dims from the source CSV |
Long format: one row per `(target_date, [dim values…], quantile)`. A
forecaster providing the median plus 50%/80%/95% intervals emits 7 rows per
date (one point + 6 quantiles). Multiple submissions from the same forecaster
land as separate parquet files under `data/`.
## Forecast targets
Truth columns from `EPI-Eval/global-mobility` you can forecast:
- `retail_and_recreation` (percent change vs. baseline)
- `grocery_and_pharmacy` (percent change vs. baseline)
- `parks` (percent change vs. baseline)
- `transit_stations` (percent change vs. baseline)
- `workplaces` (percent change vs. baseline)
- `residential` (percent change vs. baseline)
## Submitting
The dashboard at [apart-forecasting-tool](https://github.com/ChrisHarig/apart-forecasting-tool)
handles the full submission flow: drag-drop a CSV, pick this dataset as the
"Compare to" target, review your scores against the truth, and click "Submit
to HuggingFace." The dashboard serializes your CSV into the schema above and
opens a community PR here.
## Notes
- Predictions whose `target_date` falls outside the truth dataset's coverage
(forecast horizon) are still accepted. Comparison metrics on the dashboard
compute only on the dates where truth is available.
- A submission's `submitter` value is its only identity claim — there's no
signed authentication. Reviewers should sanity-check unfamiliar submitters.
_Initialized by `upload_pipeline.core.bootstrap_predictions_repos`._
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