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pith:DE5GCOGU

pith:2026:DE5GCOGUKBX6ITV6RNIFSW55M3

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SeesawNet: Towards Non-stationary Time Series Forecasting with Balanced Modeling of Common and Specific Dependencies

Hao Li, Liu Chong, Lu Zhang, Pengyang Wang, Yankai Chen, Yingjie Zhou

SeesawNet adaptively balances common and instance-specific dependencies in non-stationary time series forecasting.

arxiv:2605.14551 v1 · 2026-05-14 · cs.LG

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Claims

C1strongest claim

SeesawNet consistently outperforms state-of-the-art methods on multiple real-world benchmarks by dynamically balancing common and instance-specific dependency modeling in both temporal and channel dimensions through ASNA.

C2weakest assumption

That the adaptive fusion of common dependencies from normalized sequences and specific dependencies from raw sequences, guided by instance-level non-stationarity, will reliably capture the required heterogeneity without introducing new smoothing artifacts or overfitting to training distribution shifts.

C3one line summary

SeesawNet dynamically balances common and instance-specific dependencies via ASNA in temporal and channel dimensions, outperforming prior methods on non-stationary forecasting benchmarks.

References

21 extracted · 21 resolved · 2 Pith anchors

[1] From dense to sparse: Event response for en- hanced residential load forecasting.IEEE Transactions on Instrumentation and Measurement, 2025

[2] Pathformer: Multi-scale trans- formers with adaptive pathways for time series forecast- ing.arXiv preprint arXiv:2402.05956, 2024

[3] Dish-ts: a general paradigm for alleviating distribution shift in time series forecasting 2023

[4] Deep frequency derivative learning for non-stationary time series forecasting.arXiv preprint arXiv:2407.00502, 2024

[5] Sin: Selective and interpretable normalization for long- term time series forecasting 2024

Receipt and verification

First computed	2026-05-17T23:39:05.706680Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

193a6138d4506fe44ebe8b50595bbd66e3b1b84ae53a25f73a165089d99b315f

Aliases

arxiv: 2605.14551 · arxiv_version: 2605.14551v1 · doi: 10.48550/arxiv.2605.14551 · pith_short_12: DE5GCOGUKBX6 · pith_short_16: DE5GCOGUKBX6ITV6 · pith_short_8: DE5GCOGU

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/DE5GCOGUKBX6ITV6RNIFSW55M3 \
  | 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: 193a6138d4506fe44ebe8b50595bbd66e3b1b84ae53a25f73a165089d99b315f

Canonical record JSON

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    "abstract_canon_sha256": "8379243c2969845b2c5bb1d5733afdaf32f9bb940ed85d639ba61553aa72bdb3",
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    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-14T08:29:52Z",
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