README.md

---
license: apache-2.0
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
dataset_info:
  features:
  - name: name
    dtype: string
  - name: seed
    dtype: int64
  - name: weight
    dtype: string
  - name: context_sources
    sequence: string
  - name: skills
    sequence: string
  - name: background
    dtype: string
  - name: scenario
    dtype: string
  - name: constraints
    dtype: string
  - name: seasonal_period
    dtype: int64
  - name: past_time
    dtype: string
  - name: future_time
    dtype: string
  - name: metric_scaling
    dtype: float64
  - name: region_of_interest
    sequence: int64
  - name: constraint_min
    dtype: float64
  - name: constraint_max
    dtype: float64
  - name: constraint_variable_max_index
    sequence: int64
  - name: constraint_variable_max_values
    sequence: float64
  splits:
  - name: test
    num_bytes: 1513965
    num_examples: 355
  download_size: 213607
  dataset_size: 1513965
task_categories:
- time-series-forecasting
language:
- en
pretty_name: Context is Key
size_categories:
- n<1K
---
# Context is Key dataset

This dataset contains the samples from the [Context is Key benchmark](https://arxiv.org/abs/2410.18959).

While we encourage users of the benchmark to instance it using its [Code repository](https://github.com/ServiceNow/context-is-key-forecasting),
we understand that using this dataset can be more convenient.

## Splits

Context is Key is meant to be used as a benchmark, with only a test split.
Therefore, the splits in this dataset have been used to represent versions of the dataset, from correcting minor errors found after its initial release.

* **test**: The latest version of the dataset.
* **ICML2025**: The version of the dataset used for the experiments whose results have been published to ICML 2025.

The differences between **test** and **ICML2025** are in the `FullCausalContextImplicitEquationBivarLinSVAR` and `FullCausalContextExplicitEquationBivarLinSVAR` tasks,
where the context contained unscaled numbers in **ICML2025** and scaled numbers in **test**.

## Features

| Feature    | Content |
| -------- | ------- |
| name | The name of the task, also the name of the class generating the task in the [code](https://github.com/ServiceNow/context-is-key-forecasting) |
| seed | An integer between 1 and 5, to distinguish various instances of the same task |
| weight | A fraction indicating the relative weight this task has in aggregated RCRPS results |
| context_sources | A list of strings indicating whether the context contains past, future, causal, ... information |
| skills | A list of strings indicating skills which should help models accurately solve the task |
| background | Part of the textual context (mostly the part which doesn't depend on the instance) |
| scenario | Part of the textual context (mostly the part which does depend on the instance) |
| constraints | Part of the textual context (explicit constraints on valid forecasts) |
| seasonal_period | A reasonable guess on the seasonal period of the time series, for models which requires it. -1 if there is seasonal periodicity. |
| past_time | Pandas DataFrame converted to JSON containing the historical portion of the time series |
| future_time | Pandas DataFrame converted to JSON containing the portion of the time series to be forecasted |
| metric_scaling | Multiplier of the RCPRS metric, to handle the changes in scales between tasks |
| region_of_interest | List of indices of the future_time which should have more weight in the RCPRS metric |
| constraint_min | Any forecasted values below this value will be penalized in the RCPRS metric |
| constraint_max | Any forecasted values above this value will be penalized in the RCPRS metric |
| constraint_variable_max_index | A list of indices for which there is a maximum constraint |
| constraint_variable_max_values | A list of maximum values, any forecasted values at the associated indices will lead to a penalty in the RCPRS metric |

Users of the benchmark should only gives the *background*, *scenario*, *constraints*, *seasonal_period*, and *past_time* features to their model,
together with the timestamps of *future_time*.
The other features are there to compute the RCPRS metric and classification of the tasks.

Note: to convert *past_time* and *future_time* to Pandas DataFrame, use the following snipet: `pd.read_json(StringIO(entry["past_time"]))`.

## Computing the RCPRS metric

Code to compute the RCPRS metric is available in the [`compute_rcrps_with_hf_dataset.py`](https://huggingface.co/datasets/ServiceNow/context-is-key/blob/main/compute_rcrps_with_hf_dataset.py) script inside this dataset repository.
Please look at the `__main__` section of the script to see an example on how to use it.

## Licenses of the original data

The time series data contained in this dataset has been created using various public datasets that are either in the Public Domain or licensed under CC-BY-4.0.

* [Fire statistics for the city of Montréal](https://donnees.montreal.ca/dataset/2fc8a2b9-1556-410e-a118-c46e97e9f19e/resource/71e86320-e35c-4b4c-878a-e52124294355/download/donneesouvertes-interventions-sim.csv): CC-BY-4.0.
* [Data collected from *causal chambers*](https://github.com/juangamella/causal-chamber): CC-BY-4.0.
* [Electrical energy consumption](https://zenodo.org/records/3898439): CC-BY-4.0.
* [ATM cash withdrawal](https://zenodo.org/records/3889740): CC-BY-4.0.
* [Irradiance and weather data](https://nsrdb.nrel.gov/data-viewer): CC-BY-4.0.
* [Retail data](https://zenodo.org/records/4654802): CC-BY-4.0.
* [Solar energy production](https://zenodo.org/records/4656144): CC-BY-4.0.
* [USA unemployment](https://fred.stlouisfed.org/series/AUST448URN) (link points to only one of downloaded series): Public domain.
* [California traffic data](https://pems.dot.ca.gov/): Public domain.