Datasets:

TSFM-ai
/

future-ts

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
benchmark_id: string
name: string
version: string
description: string
adaptation_budgets: list<item: string>
  child 0, item: string
tier_weights: struct<public_dev: double, blind_archive: double, live: double>
  child 0, public_dev: double
  child 1, blind_archive: double
  child 2, live: double
tracks: list<item: string>
  child 0, item: string
task_paths: list<item: string>
  child 0, item: string
require_pretraining_manifest: bool
to
{'benchmark_id': Value('string'), 'name': Value('string'), 'version': Value('string'), 'description': Value('string'), 'adaptation_budgets': List(Value('string')), 'tier_weights': {'public_dev': Value('float64'), 'blind_archive': Value('float64'), 'live': Value('float64')}, 'tracks': List(Value('string')), 'task_paths': List(Value('string'))}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2815, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2352, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 310, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 130, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              benchmark_id: string
              name: string
              version: string
              description: string
              adaptation_budgets: list<item: string>
                child 0, item: string
              tier_weights: struct<public_dev: double, blind_archive: double, live: double>
                child 0, public_dev: double
                child 1, blind_archive: double
                child 2, live: double
              tracks: list<item: string>
                child 0, item: string
              task_paths: list<item: string>
                child 0, item: string
              require_pretraining_manifest: bool
              to
              {'benchmark_id': Value('string'), 'name': Value('string'), 'version': Value('string'), 'description': Value('string'), 'adaptation_budgets': List(Value('string')), 'tier_weights': {'public_dev': Value('float64'), 'blind_archive': Value('float64'), 'live': Value('float64')}, 'tracks': List(Value('string')), 'task_paths': List(Value('string'))}
              because column names don't match

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FUTURE-TS

Summary

FUTURE-TS v0.1.0 is a public-preview benchmark for time-series foundation models. It treats evaluation as an executable protocol: task cards declare issue times, horizons, available history, delayed labels, adaptation budgets, resource limits, and metrics; submissions are validated, scored, and audited against those contracts.

The core rule is future-only ranking: a submission is only ranked on labels that were not available when the model was frozen.

Benchmark Surface

Release: v0.1.0
Strict surface: benchmarks/v1/benchmark.json
Surface identifier: future-ts-v1
Tasks: 25
Tiers: public_dev, blind_archive, live
Tracks: forecasting core, covariate-aware, multivariate relational, data quality, event, transfer, multimodal context
Required manifest: require_pretraining_manifest=true
Headline score: tier_weighted_score
Additional views: capability vector (F, U, R, A, E, D), Pareto flag, rank-based mean-of-ranks aggregate with bootstrap CI

The strict surface rejects manifestless submissions so "clean" and "undeclared" are not conflated.

Current Empirical Run

The current canonical empirical artifacts are in:

reports/tsfm_ai_empirical_v2_multi_budget/

This run is the future-ts-empirical-v2 hosted TSFM.ai slice used by the empirical paper. It exercises 15 real-data tasks across three context budgets:

Budget	Context length
`zero_shot`	96
`few_shot`	192
`s16`	288

Current result snapshot:

Surfaced public catalog entries: 52
Merged public submissions: 51
Scored public models: 47
Positive overall scores: 30
Current winner: Datadog/Toto-2.0-1B
Winner score: 0.2369
Rank-consistency leader by mean rank: amazon/chronos-2

Top five by tier-weighted score:

Rank	Model	Score
1	`Datadog/Toto-2.0-1B`	0.2369
2	`Datadog/Toto-2.0-313m`	0.2214
3	`NX-AI/TiRex`	0.2024
4	`Salesforce/moirai-2.0-R-small`	0.1860
5	`amazon/chronos-2`	0.1851

How To Submit A Model Entry

Submit a pull request under:

submissions/community/<org>_<model>/

Each submission directory must contain:

script.py: sealed-runner entry point
declaration.json: metadata, declarations, artifact URI, and pretraining manifest
README.md: short model description and links

CI validates the directory structure and smoke-runs script.py through the sealed runner. After review, the evaluator runs the full benchmark and can publish the resulting BenchmarkReport.

See submission-guide.md for the full process.

What This Does Not Yet Claim

The v0.1.0 release is a local benchmark package plus sealed-runner MVP. It is not yet a fully hosted, externally attested live benchmark service.

Do not read the current empirical run as a permanent universal TSFM ranking. It is a first real-data slice over 15 tasks and one materialized wave. Wider task coverage, repeated waves, hosted attestation, immutable submission windows, and stronger manifest evidence are the next steps.

See validity-envelope.md for the precise claim boundary.

Local Commands

Validate the strict benchmark:

python3 -m future_ts.cli validate-benchmark benchmarks/v1/benchmark.json

Run the sealed reference submission:

python3 -m future_ts.cli run-sealed \
  examples/submissions/reference_seasonal_naive.py \
  .tmp/task_windows.json \
  --output .tmp/submission.json \
  --submission-id local::reference_seasonal_naive \
  --model-name "Reference Seasonal Naive"

Build a leaderboard from saved reports:

python3 -m future_ts.cli leaderboard reports/tsfm_ai_empirical_v2_multi_budget/*.report.json

These core workflows do not require TSFM.ai. A TSFM.ai API key is only needed to run the optional hosted-catalog evaluation commands that invoke hosted models.

More Detail

Design paper: paper/future_ts_design.pdf
Empirical paper: paper/future_ts_empirical.pdf
Benchmark design notes: benchmark-design.md
Submission guide: submission-guide.md
Sealed runner: sealed-runner.md
Validity envelope: validity-envelope.md

Repository Contents

benchmarks/: public benchmark surfaces and task-card references.
schemas/: JSON Schemas for benchmark, task, submission, and actual records.
docs/: benchmark card, submission guide, sealed-runner notes, and validity envelope.
reports/tsfm_ai_empirical_v2_multi_budget/: canonical public empirical reports.
paper/: design and empirical PDFs.

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