Pith Number
pith:BRBUVDDO
pith:2022:BRBUVDDOW2WQLXZVAXQKDQGJS4
not attested
not anchored
not stored
refs pending
Approaching sales forecasting using recurrent neural networks and transformers
arxiv:2204.07786 v1 · 2022-04-16 · cs.LG · cs.AI · stat.AP
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{BRBUVDDOW2WQLXZVAXQKDQGJS4}
Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge
Record completeness
1
Bitcoin timestamp
2
Internet Archive
3
Author claim
· sign in to
claim
4
Citations
5
Replications
✓
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.
Receipt and verification
| First computed | 2026-07-05T04:15:18.539544Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
0c434a8c6eb6ad05df3505e0a1c0c99735f8ec78131342fdfbf83ce0c1d40351
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/BRBUVDDOW2WQLXZVAXQKDQGJS4 \
| 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: 0c434a8c6eb6ad05df3505e0a1c0c99735f8ec78131342fdfbf83ce0c1d40351
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "36c5c10217ae2094effd2e4beb0ce6052939067cd2568352de1cb14ee120faf9",
"cross_cats_sorted": [
"cs.AI",
"stat.AP"
],
"license": "http://creativecommons.org/publicdomain/zero/1.0/",
"primary_cat": "cs.LG",
"submitted_at": "2022-04-16T12:03:52Z",
"title_canon_sha256": "be9e8997f05feefe7c171dfc866744f23410519f39bd808fd5967ba8cd89fcc8"
},
"schema_version": "1.0",
"source": {
"id": "2204.07786",
"kind": "arxiv",
"version": 1
}
}