Saxon0520/LiveHouse-TS-test

LiveHouse-TS

LiveHouse-TS is a prospective benchmark for univariate time-series forecasting. Models receive only observations that were publicly available at the forecast cutoff. Their predictions are frozen before the target window begins and scored after the complete target becomes available.

Website: https://huggingface.co/spaces/Saxon0520/LiveHouse-TS-test · Public data: https://huggingface.co/datasets/Saxon0520/LiveHouse-TS-test · Source: https://github.com/ATMSaxon/LiveHouse-TS-test

Core workflow

public sources -> normalized observations -> frozen forecast
               -> future observations arrive -> metrics -> public leaderboard

The public SDK has five modules:

• data.py: streaming-data adapters and SQLite ingestion;
• data_schema.py: canonical objects and schema v1;
• models.py: the model protocol, hosted adapters, and four statistical baselines;
• metrics.py: versioned point and probabilistic metrics;
• eval.py: forecast freezing, scoring, and public export.

Runtime data, model weights, raw responses, frozen forecasts, and result files are not stored in this Git repository.

Quick start

git clone https://github.com/ATMSaxon/LiveHouse-TS-test.git
cd LiveHouse-TS
python -m venv .venv
source .venv/bin/activate
python -m pip install -e '.[dev,baselines]'
pytest -q

Create or open a schema-v1 database:

from livehouse_ts.data_schema import connect

database = connect("livehouse.sqlite")

Implement DataSource.fetch() to return a normalized DataBatch, then call collect(source, SQLiteRepository(database)). A data adapter is responsible for recording event time, public availability time, and ingestion time separately.

Run one cycle against the seven currently enabled data streams:

livehouse-ts-cycle private/livehouse.sqlite public

This collects past observations, resolves due tasks, issues dataset-specific forecasts, and writes public CSV/JSON artifacts. Seasonal Naive is always included. Add HTTPS models through LIVEHOUSE_MODELS_JSON:

[{"model_id":"organization/model","endpoint_url":"https://forecast.example.org/forecast"}]

Join the benchmark

Paper model roster

Self-hosting status: the eight foundation models are not yet self-hosted. The adapters below are optional hosted-provider integrations, not completed local deployments. GPU/server configuration and actual weight loading remain deferred. Do not enable paper to claim self-hosted results.

LIVEHOUSE_MODEL_SET=baselines enables Seasonal Naive, Moving Average (24), ARIMA(1,1,1), and ETS (additive trend, no seasonality). LIVEHOUSE_MODEL_SET=paper additionally enables all eight foundation models:

Model	Provider / original registry identifier
Chronos-2	TSFM.ai: amazon/chronos-2
TiRex	TSFM.ai: NX-AI/TiRex-1.1-gifteval
TimesFM-2.5	TSFM.ai: google/timesfm-2.5-200m-pytorch
Toto-1.0	TSFM.ai: Datadog/Toto-Open-Base-1.0
Moirai-2.0	TSFM.ai: Salesforce/moirai-2.0-R-small
Chronos-Bolt	TSFM.ai: amazon/chronos-bolt-base
Sundial	TSFM.ai: thuml/sundial-base-128m
TabPFN-TS	Prior Labs client: priorlabs/tabpfn-ts

Install .[baselines] for statistical inference and additionally .[tabpfn] for the paper roster. Core imports remain dependency-light. The TabPFN package versions match the previous repository's client environment; its hosted checkpoint is provider-controlled, so this does not establish exact reproduction of the paper's historical weights. The other hosted model IDs are also not immutable weight hashes.

In Actions, set secrets TSFM_API_KEY and TABPFN_TOKEN, then manually run Validate paper models with paper. This makes real provider calls on a synthetic series, uses inference quota, and does not publish benchmark results. Only after it passes, set repository variable LIVEHOUSE_MODEL_SET=paper to enable the hourly operator. minimal is the default and keeps the reference baseline plus explicitly configured HTTPS endpoints. Missing credentials fail before an operator run can change HF data.

These are new schema-v1 runs, not imported paper scores. Statistical models use a deterministic 200-sample centered residual bootstrap; failed fits are recorded as failures, never silently replaced by a different model. TiRex asks for at least 24 future steps and retains the requested prefix, matching the old registry's hosted-horizon workaround. The persistence demo is not a paper model.

Submit your own model

Participants host their model behind one small HTTP API. Start from examples/model_api, replace forecast_one, and expose:

GET  /health
POST /forecast

The request contains opaque series IDs, historical timestamps and values, frequency, horizon, and requested quantiles. It never contains future targets, private metrics, dataset-internal IDs, or another model's predictions. Remote endpoints must use stable HTTPS.

Each input must have one output with prediction_length finite numeric values in mean and in each requested quantile (0.1 through 0.9 in steps of 0.1). Quantiles must be nondecreasing at each time step. Point-only models may repeat their point forecast at every quantile, as the demo does; this is a degenerate distribution, not a calibrated uncertainty estimate. Admission and live evaluation apply the same checks before storing predictions.

Validate an endpoint before submission:

livehouse-ts-validate organization/model https://forecast.example.org/forecast

Submit the following through the GitHub community-model form:

1. model ID and display name;
2. model card URL;
3. public endpoint-code URL;
4. stable HTTPS /forecast URL;
5. version and organization.

After endpoint validation, maintainers record an admission timestamp. A model is evaluated only on tasks created after admission; no historical backfill is used.

Evaluation protocol

For each task, LiveHouse-TS:

1. selects context whose available_time is no later than the task cutoff;
2. calls every admitted model with the same context and horizon;
3. freezes predictions before target_start;
4. waits until the target window ends and every target value is available;
5. computes metrics with a recorded metric version;
6. publishes only schema-v1 aggregate results and bounded visual examples.

Old pre-schema results are intentionally excluded from the new leaderboard.

Evaluation v2 checks the clock both before and after inference, rejects late responses, verifies regular context/target grids, and forecasts across the gap between the last available context and the future target before slicing out the scored horizon. Context must have both availability and ingestion times no later than cutoff. The CLI does not accept backdated issue times. The first successful scoring of a run is immutable; later source revisions do not replace it.

Data coverage

Enabled stream	Value type	Native frequency	Context / forecast steps
Open-Meteo Shanghai temperature	Model estimate, not station truth	1h	336 / 24
Open-Meteo Shanghai PM2.5	Model estimate, not station truth	1h	336 / 24
USGS Potomac discharge	Gauge measurement	5min (current API; paper lists 15min)	288 / 72
NOAA San Francisco water level	Station measurement	6min	240 / 60
Wikimedia Time series pageviews	Reported count	1d	30 / 7
NASA POWER Shanghai temperature	Gridded meteorological model estimate	1h (explicit UTC)	336 / 24
USGS global earthquake catalogue counts	Reported event count, complete hours	1h	168 / 24

Availability is conservatively recorded at first ingestion, not inferred from event time. Irregular or missing grids are rejected, not silently filled. These seven streams do not yet reproduce the paper's full 17-dataset inventory. Window lengths live in data.py and travel with each dataset's metadata. The operator requires the full configured context; shorter histories appear in status.json.pending_context and do not create undersized tasks. Unregistered custom adapters retain the 168/6 default (minimum 24 points). USGS water uses 288/72 to preserve the paper's 24-hour context and six-hour horizon at its current five-minute cadence. Source polling remains hourly, independently of native frequency. Exported release rows record actual horizon and frequency. Earlier six-step tasks keep their original definitions and results; new tasks have window lengths in their identity, so changing a window never rewrites an existing task. Aggregates currently include both old and new task windows.

NASA is requested in UTC, not the API's default local solar time, and its fill value is excluded. Its release delay is handled as a forecast gap, not hidden. The earthquake weekly feed usually provides 167 complete hours on first fetch; the 168-step context warms up as subsequent hourly collections accumulate. Zero counts are emitted only inside complete feed coverage, and partial edge hours are excluded. Feed truncation/duplicate event IDs are rejected.

The NCEI USW00014732 TAVG adapter is implemented but not enabled: the 2026-09-26 check found 87 records in a 90-day request with no daily mean values. It fails explicitly rather than substituting TMAX/TMIN. Choosing a replacement station requires a dataset-definition decision. Nine other paper streams still need adapters: NOAA NDBC, GBFS, three Binance frequencies, CoinGecko, NWS, World Bank, and GDELT. Second-level collection and calendar-frequency task construction still need dedicated scheduling/support.

Metrics and ranking

Metric v2 computes MSE, RMSE, MAE and quantile-approximate CRPS using the context population standard deviation only (unit scale for a near-constant context). MAPE uses the median forecast in original units and is omitted near zero targets. CRPS uses the nine equally spaced quantiles 0.1–0.9 from paper Appendix E.

Official ranking follows Appendix E: compare MSE and CRPS separately on shared releases, average the two win/tie/loss outcomes, then macro-average by dataset and eligible opponent. Each pair needs 30 shared releases, five datasets and seven days of target-time span. A model needs three eligible opponents and membership in the largest connected comparison component to receive an official rank. Ties share rank; missing comparisons are not ties. Otherwise status is provisional, awaiting forecasts/scores, or failed.

The lower-is-better normalized composite score remains a diagnostic, not the official ranking key; zero-error baseline denominators are omitted from this diagnostic only. Diagnostic Elo starts at 1500, K=16 per shared-task pair, with simultaneous updates and deterministic target-time/task-ID ordering. It is not the official rank and the paper does not specify these exact Elo parameters. Average rank uses tied shared-task composite ranks, macro-averaged by dataset. RTG is 100*(baseline MSE-model MSE)/(baseline MSE+model MSE), averaged by dataset (both zero gives zero). Stability is the sample standard deviation of daily dataset-balanced normalized composite scores. Improvement is Kendall tau-a of normalized squared errors for repeated predictions of the same target and truth revision, averaged by target and dataset; fewer than two predictions gives no value. Coverage uses only reference tasks after the model's admission.

These implementation choices are explicit; this repository does not yet claim full numerical reproduction of every paper table or the original experiment.

Storage and deployment

The private Hugging Face Dataset contains the canonical SQLite database, normalized observations, tasks, and frozen forecasts. The public Dataset contains stable CSV, JSON, and metrics-only release exports. The Hugging Face Space is a read-only presentation layer with no token or private-data access. A single evaluator writer updates Saxon0520/LiveHouse-TS-test-private-data first, then publishes derived artifacts to the public Saxon0520/LiveHouse-TS-test Dataset using optimistic commit checks.

GitHub Actions provides an hourly single-writer operator and a manual HF resource/Space deployment workflow. Both use the repository secret HF_TOKEN. Optional external models are stored in LIVEHOUSE_MODELS_JSON. The Static Space itself receives no secret.

Run Verify Hugging Face backup in GitHub Actions to check recovery without changing either Dataset. It restores the private SQLite revision referenced by the public export, checks database integrity and foreign keys, and reproduces the four public CSV tables and leaderboard JSON (except its generation time). The run reports counts only; it does not upload the private database. The public Dataset exposes leaderboard, datasets, models, and releases as separate Dataset Viewer configurations.

Historical ranks and Backtesting Archive

The Space includes Elo and composite-score comparison charts, daily rank history, and a version selector that loads the table, datasets, and forecast example from the same pinned public revision. Elo is displayed separately; current primary rank uses eligible pairwise wins. Archived v1 results retain their former composite-score ordering and must not be compared as one ranking regime with evaluation v2.

The existing hourly operator adds one snapshot per UTC day to history.json, using the previously published HF commit. The first captured version for a day is retained unchanged. Each archive entry links to that commit's leaderboard, release scores, forecast example, and private-source revision reference; it does not publish the private database. These are archived prospective results, not retrospective inference or imported paper scores. Archiving starts when this feature is deployed; outages leave gaps instead of fabricated history. The chart shows the latest 30 archived days; older versions remain selectable and downloadable. HF commit history must be retained for archive links to work.

LiveHouse series

This repository is intentionally scoped to univariate time series. Future spatio-temporal graph or multivariate benchmarks will use separate LiveHouse projects while sharing versioned concepts such as datasets, entities, variables, tasks, models, and releases.

Update log

0.2.0 — evaluation v2 / metrics v2 (SQLite schema remains v1)

• Added dataset-specific task windows with explicit history warm-up status.

• Added NASA POWER UTC parsing and complete-hour USGS earthquake counts.

• Kept NCEI daily mean temperature disabled while its target field is absent.

• Enforced inference completion deadlines and gap-aware timestamp alignment.

• Separated new results from legacy scores without deleting historical data.

• Added nine-quantile context-normalized scoring and paper eligibility rules.

• Corrected RTG, temporal stability, fixed-target Improvement, and tied ranks.

• Kept unscored models visible and labelled data provenance.

• Added USGS discharge, NOAA water level and Wikimedia pageviews adapters.

0.1.0 — schema v1

• Reduced the public project to five core modules and one HTTP model example.
• Added canonical SQLite storage with separate event, availability and ingest times.
• Added the livehouse-ts-v1 model and metric protocols.
• Added two real Open-Meteo streams and the hourly single-writer operator.
• Added normalized ranking, win rate, Elo, RTG, stability and availability.
• Started a new leaderboard containing only schema-v1 releases.

Future schema, protocol, metric, data-source, or ranking changes receive an explicit version entry here.

Citation

@misc{livehouse_ts,
  title  = {LiveHouse-TS: A Live Benchmark for Time-Series Forecasting},
  author = {ThinkCat Lab},
  year   = {2026},
  url    = {https://github.com/ATMSaxon/LiveHouse-TS-test}
}

Apache-2.0 licensed. LiveHouse-TS builds on the prospective evaluation direction of GIFT-Eval while maintaining its own live-data schema and protocol.