ServiceNow/context-is-key

Context is Key dataset

This dataset contains the samples from the Context is Key benchmark.

While we encourage users of the benchmark to instance it using its Code repository, 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
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 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: CC-BY-4.0.
• Data collected from causal chambers: CC-BY-4.0.
• Electrical energy consumption: CC-BY-4.0.
• ATM cash withdrawal: CC-BY-4.0.
• Irradiance and weather data: CC-BY-4.0.
• Retail data: CC-BY-4.0.
• Solar energy production: CC-BY-4.0.
• USA unemployment (link points to only one of downloaded series): Public domain.
• California traffic data: Public domain.