Pith Number

pith:CNRQVHLG

pith:2026:CNRQVHLGUZDMK2SLBBPWS7JYRJ

not attested not anchored not stored refs resolved

SSDA: Bridging Spectral and Structural Gaps via Dual Adaptation for Vision-Based Time Series Forecasting

Hanchen Yang, Jihong Guan, Mingrui Zhang, Shuigeng Zhou, Wengen Li, Xudong Jiang, Yichao Zhang

SSDA adapts pre-trained vision models for time series by closing spectral and structural gaps in rendered images.

arxiv:2605.12550 v1 · 2026-05-10 · cs.CV · cs.AI

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\pithnumber{CNRQVHLGUZDMK2SLBBPWS7JYRJ}

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3 Author claim open · sign in to claim

4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Extensive experiments on seven real-world benchmarks demonstrate that SSDA consistently outperforms strong LVM- and LLM-based baselines under both full-shot and few-shot settings.

C2weakest assumption

The assumption that the identified spectral and structural gaps are the main factors limiting transfer from natural-image pre-training and that the proposed SMA and SG-LoRA modules close these gaps without introducing new biases or overfitting.

C3one line summary

SSDA uses spectral magnitude alignment and structural-guided low-rank adaptation to close frequency and adjacency gaps when large vision models process time series rendered as images.

References

50 extracted · 50 resolved · 0 Pith anchors

[1] Fredn: Spectral disentanglement for time series forecasting via learnable frequency decomposition 2026

[2] Visionts: Visual masked autoencoders are free-lunch zero-shot time series forecasters 2025

[3] Sparse learned kernels for interpretable and efficient medical time series processing.Nature machine intelligence, 6(10):1132–1144, 2024 2024

[4] Imagenet: A large- scale hierarchical image database 2009

[5] An image is worth 16x16 words: Transformers for image recognition at scale 2021

Receipt and verification

First computed	2026-05-18T03:10:02.139360Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

13630a9d66a646c56a4b085f697d388a571697fbffde5f4f3fe9f1627f4aa712

Aliases

arxiv: 2605.12550 · arxiv_version: 2605.12550v1 · doi: 10.48550/arxiv.2605.12550 · pith_short_12: CNRQVHLGUZDM · pith_short_16: CNRQVHLGUZDMK2SL · pith_short_8: CNRQVHLG

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/CNRQVHLGUZDMK2SLBBPWS7JYRJ \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 13630a9d66a646c56a4b085f697d388a571697fbffde5f4f3fe9f1627f4aa712

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "b08509fbd09435c3b9918aad4dbe6655fe53f27547205f67fad12b8f5609e6cf",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-10T07:17:08Z",
    "title_canon_sha256": "c5758fd13902829afb12781482da012c4be2f2890a56a9311c578dd9f7172657"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.12550",
    "kind": "arxiv",
    "version": 1
  }
}