{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "You already need task-specific training and want to reduce its memory or compute cost.",
    "change": "Change only the training technique on a small controlled run after checking prerequisites.",
    "baseline": "Your current training recipe with the same model, data split, and evaluation.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Held-out quality, peak memory, training time, and full compute cost to the target quality.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2610.28200",
  "title": "Parameter-Efficient Adaptation of Time Series Foundation Models for Electrical Load Forecasting",
  "summary": "Replaces training target-specific forecasting models from scratch and avoids the memory and compute cost of full fine-tuning of a pretrained time series foundation model. Costs roughly 1% of model parameters in trainable weights plus labeled target-domain data, with quality that varies by model family and target series. Can fail when the pretrained checkpoint is already strong for the target (as with Chronos-2), when OOD targets show no adaptation gain, or when target data is too scarce to tune even low-rank adapters.",
  "rationale": "The method runs on a single 24 GB GPU and offers a validated accuracy-efficiency trade-off, but gains are target- and backbone-dependent, so workload-specific validation on the reader's own series is required before production adoption.",
  "technique": "LoRA adaptation of time series foundation models",
  "family": "treino_eficiente",
  "recommendation": "testar",
  "published": "2026-10-20",
  "as_of": "2026-10-21",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.28200",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "Replaces training target-specific forecasting models from scratch and avoids the memory and compute cost of full fine-tuning of a pretrained time series foundation model. Costs roughly 1% of model parameters in trainable weights plus labeled target-domain data, with quality that varies by model family and target series. Can fail when the pretrained checkpoint is already strong for the target (as with Chronos-2), when OOD targets show no adaptation gain, or when target data is too scarce to tune even low-rank adapters.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2610.28200",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Rank-16 LoRA on MOIRAI Large reduces NMAE by 7.5% on average across four load aggregation levels while updating 3.146M parameters (~1% of the model), retaining most of full fine-tuning gains; Chronos-2 starts better but benefits less from adaptation.",
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      "source_url": "https://arxiv.org/abs/2610.28200",
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    }
  ],
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    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "source_page": null,
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    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
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}