Pith Number

pith:7ZLTME4S

pith:2026:7ZLTME4STBAETP4MTKRCD5I4H6

not attested not anchored not stored refs resolved

PULSE: Generative Phase Evolution for Non-Stationary Time Series Forecasting

Fei Wang, Hu Chen, Xinyu Chen, Yangyou Liu, Yuankai Wu, Zezhi Shao

A simple MLP equipped with physical phase evolution modeling matches or exceeds complex models on non-stationary time series forecasting.

arxiv:2605.16793 v1 · 2026-05-16 · cs.LG

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\pithnumber{7ZLTME4STBAETP4MTKRCD5I4H6}

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Record completeness

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

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

PULSE enables a simple MLP backbone to achieve state-of-the-art or highly competitive performance across 12 real-world benchmarks. This validates that a correct physics-informed inductive bias is far more critical than raw architectural complexity for non-stationary forecasting.

C2weakest assumption

The three physical hypotheses (Wold decomposition, dynamical phase evolution, and heteroscedastic manifold generation) provide a valid and useful formalization of non-stationary dynamics that directly translates into an effective forecasting architecture.

C3one line summary

PULSE formalizes non-stationary time series via three physical hypotheses and uses phase-anchored disentanglement plus a Phase Router to let a simple MLP reach competitive performance on 12 benchmarks.

References

51 extracted · 51 resolved · 0 Pith anchors

[1] International conference on learning representations , year=

[2] IEEE Transactions on Knowledge and Data Engineering , year=

[3] The Thirteenth International Conference on Learning Representations , year=

[4] Proceedings of the 30th ACM international conference on information & knowledge management , pages=

[5] Information geometry and its applications , author=. 2016 , publisher= 2016

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:03:22.358482Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

fe57361392984049bf8c9aa221f51c3fbcdbac0856d3f8342e1421e1d66d0ee6

Aliases

arxiv: 2605.16793 · arxiv_version: 2605.16793v1 · doi: 10.48550/arxiv.2605.16793 · pith_short_12: 7ZLTME4STBAE · pith_short_16: 7ZLTME4STBAETP4M · pith_short_8: 7ZLTME4S

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/7ZLTME4STBAETP4MTKRCD5I4H6 \
  | 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: fe57361392984049bf8c9aa221f51c3fbcdbac0856d3f8342e1421e1d66d0ee6

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "895eb008a525e2b3dbcb3fc445ed94d84aeb40b898f666e2d95cf46b526422a3",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-16T03:54:18Z",
    "title_canon_sha256": "5cedf2c6d8bd49082c065f1dcdb09e66a722c8fb3ebe07490d3d8d3e5a5103ca"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16793",
    "kind": "arxiv",
    "version": 1
  }
}