QuitoBench: A High-Quality Open Time Series Forecasting Benchmark

QuitoBench: A High-Quality Open Time Series Forecasting Benchmark | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/quitobench-a-high-quality-open-time-series-forecasting-benchmark

stale

Proof freshness: stale
Proof status: unverified
Display score: 5/10
Last proof check: 2026-03-30
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 53
Source count: 3
Coverage: 50%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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Canonical ID quitobench-a-high-quality-open-time-series-forecasting-benchmark | Route /signal-canvas/quitobench-a-high-quality-open-time-series-forecasting-benchmark

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curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/quitobench-a-high-quality-open-time-series-forecasting-benchmark

MCP example

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    "query_text": "Summarize QuitoBench: A High-Quality Open Time Series Forecasting Benchmark"
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}

source_context

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}

Evidence Receipt

Route status: building

Claims: 12

References: 53

Proof: Verification pending

Freshness state: computing

Source paper: QuitoBench: A High-Quality Open Time Series Forecasting Benchmark

PDF: https://arxiv.org/pdf/2603.26017v1

Source count: 3

Coverage: 50%

Last proof check: 2026-03-30T21:57:46.775Z

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Watch and verify: QuitoBench: A High-Quality Open Time Series Forecasting Benchmark

/buildability/quitobench-a-high-quality-open-time-series-forecasting-benchmark

Watchwatch

Subject: QuitoBench: A High-Quality Open Time Series Forecasting Benchmark

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 12Mixed 0Weak 0

Evidencepartial
we introduce QuitoBench, a regime-balanced benchmark for time series forecasting with coverage across eight trend×seasonality×forecastability (TSF) regimes
Implicationpartial
This is a core definition of the benchmark presented in the abstract and introduction.
Verificationpartial
partial
Evidencepartial
The benchmark is built upon Quito, a billion-scale time series corpus of application traffic from Alipay
Implicationpartial
This describes the data source for the benchmark, explicitly stated in the abstract.
Verificationpartial
partial
Evidencepartial
a context-length crossover where deep learning models lead at short context (L=96) but foundation models dominate at long context (L ≥ 576)
Implicationpartial
This is one of the four key findings reported in the abstract and elaborated in the results section.
Verificationpartial
partial
Evidencepartial
forecastability is the dominant difficulty driver, producing a 3.64 × MAE gap across regimes
Implicationpartial
This is another key finding explicitly stated in the abstract with a quantitative measure.
Verificationpartial
partial
Evidencepartial
deep learning models match or surpass foundation models at 59 × fewer parameters
Implicationpartial
This is a key finding regarding model efficiency, clearly stated in the abstract.
Verificationpartial
partial
Evidencepartial
scaling the amount of training data provides substantially greater benefit than scaling model size for both model families
Implicationpartial
This is a key finding about the impact of data and model scaling, explicitly stated in the abstract.
Verificationpartial
partial
Evidencepartial
existing benchmarks group series by application domain (e.g., electricity, traffic, weather), yet there is no systematic justification for why these domain labels should predict forecasting difficulty.
Implicationpartial
This is presented as a weakness of prior benchmarks, motivating the need for QuitoBench.
Verificationpartial
partial
Evidencepartial
we introduce QuitoBench, a regime-balanced benchmark for time series forecasting with coverage across eight trend×seasonality×forecastability (TSF) regimes
Implicationpartial
The abstract explicitly states the benchmark's coverage of TSF regimes.
Verificationpartial
partial
Evidencepartial
The benchmark is built upon Quito, a billion-scale time series corpus of application traffic from Alipay
Implicationpartial
The abstract clearly states the origin and scale of the benchmark's data.
Verificationpartial
partial
Evidencepartial
a context-length crossover where deep learning models lead at short context (L=96) but foundation models dominate at long context (L ≥ 576)
Implicationpartial
This is presented as a key finding with specific context lengths mentioned.
Verificationpartial
partial
Evidencepartial
forecastability is the dominant difficulty driver, producing a 3.64 × MAE gap across regimes
Implicationpartial
This is stated as a key finding with a specific quantitative measure.
Verificationpartial
partial
Evidencepartial
deep learning models match or surpass foundation models at 59 × fewer parameters
Implicationpartial
This is presented as a key finding with a specific quantitative comparison.
Verificationpartial
partial

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QuitoBench: A High-Quality Open Time Series Forecasting Benchmark

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