Pith Number

pith:IZTV6GYH

pith:2025:IZTV6GYHZX2EP6V3TWUCSWMRSQ

not attested not anchored not stored refs pending

MSTN: A Lightweight and Fast Model for General TimeSeries Analysis

Chandresh K Maurya, Sumit S Shevtekar

The Multi-scale Temporal Network uses early aggregation of convolutional features, sequence modeling, and self-gated fusion to set new performance marks on time series tasks while staying under one million parameters.

arxiv:2511.20577 v4 · 2025-11-25 · cs.LG

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\pithnumber{IZTV6GYHZX2EP6V3TWUCSWMRSQ}

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Record completeness

1 Bitcoin timestamp

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

Across extensive benchmarks covering imputation, long term forecasting, short term forecasting, classification, and cross-dataset generalization, MSTN achieves state-of-the-art performance, establishing new best results on 33 of 40 datasets, while remaining lightweight (~278,520 params for MSTN-BiLSTM and ~950,776 ≈ 1M for MSTN-Transformer) and suitable for low-latency inference (<1 sec, often in milliseconds).

C2weakest assumption

That the specific combination of multi-scale convolutional encoder, sequence module, and self-gated fusion generalizes to new time series distributions without requiring extensive per-dataset hyperparameter retuning or suffering from benchmark overfitting.

C3one line summary

MSTN is a lightweight hybrid model that reports new state-of-the-art results on 33 of 40 time series benchmarks for imputation, forecasting, and classification while using under one million parameters and sub-second inference.

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:02:59.542821Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

46675f1b07cdf447fabb9da8295991940f74afadf9578bf399232ece90dc2de5

Aliases

arxiv: 2511.20577 · arxiv_version: 2511.20577v4 · doi: 10.48550/arxiv.2511.20577 · pith_short_12: IZTV6GYHZX2E · pith_short_16: IZTV6GYHZX2EP6V3 · pith_short_8: IZTV6GYH

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/IZTV6GYHZX2EP6V3TWUCSWMRSQ \
  | 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: 46675f1b07cdf447fabb9da8295991940f74afadf9578bf399232ece90dc2de5

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "327d99bec7165c74170b282782b6c148adafe362ae914f1f20ff547b9c20eb08",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2025-11-25T18:09:42Z",
    "title_canon_sha256": "70ca2fe13429d01dfe8edc3b4c95a4dd08d9a69aac0c5d71636ea2db3863ed3e"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2511.20577",
    "kind": "arxiv",
    "version": 4
  }
}