{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:KXJOK2NQSZOF7Z4E55FZIVJHJ6","short_pith_number":"pith:KXJOK2NQ","canonical_record":{"source":{"id":"1711.11053","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-29T19:01:32Z","cross_cats_sorted":[],"title_canon_sha256":"88c33f02b6dcd467350394c3c92ee254ffa382d087d519c19a4fd5194c5f1292","abstract_canon_sha256":"5d65551aa4eaf58c1e2b96262b0dc29d27dc920982986fc75beb179bdd2a3612"},"schema_version":"1.0"},"canonical_sha256":"55d2e569b0965c5fe784ef4b9455274f9e77d1b455b183eb162b23c0ff41d958","source":{"kind":"arxiv","id":"1711.11053","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.11053","created_at":"2026-05-18T00:12:09Z"},{"alias_kind":"arxiv_version","alias_value":"1711.11053v2","created_at":"2026-05-18T00:12:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.11053","created_at":"2026-05-18T00:12:09Z"},{"alias_kind":"pith_short_12","alias_value":"KXJOK2NQSZOF","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_16","alias_value":"KXJOK2NQSZOF7Z4E","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_8","alias_value":"KXJOK2NQ","created_at":"2026-05-18T12:31:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:KXJOK2NQSZOF7Z4E55FZIVJHJ6","target":"record","payload":{"canonical_record":{"source":{"id":"1711.11053","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-29T19:01:32Z","cross_cats_sorted":[],"title_canon_sha256":"88c33f02b6dcd467350394c3c92ee254ffa382d087d519c19a4fd5194c5f1292","abstract_canon_sha256":"5d65551aa4eaf58c1e2b96262b0dc29d27dc920982986fc75beb179bdd2a3612"},"schema_version":"1.0"},"canonical_sha256":"55d2e569b0965c5fe784ef4b9455274f9e77d1b455b183eb162b23c0ff41d958","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:12:09.080410Z","signature_b64":"ONt/S4gqcJ/ziHHp2VHHTc4PB/0/EZPd+44IkKvgxFM+HDxGflapOYaj/gAmdwayf+FrS/q3r6AVijdw5xJQDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"55d2e569b0965c5fe784ef4b9455274f9e77d1b455b183eb162b23c0ff41d958","last_reissued_at":"2026-05-18T00:12:09.079755Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:12:09.079755Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1711.11053","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:12:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dRsB1XzQqbsrrszRrv8zMkCbn/FPlqEBs336z5d4QelfuERBNe7juqovLVd7oSYSOcTE2UtBS/hZtMI/ms3JAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T22:48:13.432049Z"},"content_sha256":"953c730412ed2c09096674ce8c43b70757385f235a9d99a519e138aa28554211","schema_version":"1.0","event_id":"sha256:953c730412ed2c09096674ce8c43b70757385f235a9d99a519e138aa28554211"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:KXJOK2NQSZOF7Z4E55FZIVJHJ6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Multi-Horizon Quantile Recurrent Forecaster","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Balakrishnan Narayanaswamy, Dhruv Madeka, Kari Torkkola, Ruofeng Wen","submitted_at":"2017-11-29T19:01:32Z","abstract_excerpt":"We propose a framework for general probabilistic multi-step time series regression. Specifically, we exploit the expressiveness and temporal nature of Sequence-to-Sequence Neural Networks (e.g. recurrent and convolutional structures), the nonparametric nature of Quantile Regression and the efficiency of Direct Multi-Horizon Forecasting. A new training scheme, *forking-sequences*, is designed for sequential nets to boost stability and performance. We show that the approach accommodates both temporal and static covariates, learning across multiple related series, shifting seasonality, future pla"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.11053","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:12:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"44FuFb6Cv29SKzfEtkFR5IltcSojkm0P1VejBUgHdwcUOXvtC+1jho50C/LPRBcyhmdEUdpNFs0U6YD/CaDyDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T22:48:13.432416Z"},"content_sha256":"c155e01a0779b66111e94adb34bca9925b67d13078e2d0972078e99c5fd04b61","schema_version":"1.0","event_id":"sha256:c155e01a0779b66111e94adb34bca9925b67d13078e2d0972078e99c5fd04b61"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KXJOK2NQSZOF7Z4E55FZIVJHJ6/bundle.json","state_url":"https://pith.science/pith/KXJOK2NQSZOF7Z4E55FZIVJHJ6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KXJOK2NQSZOF7Z4E55FZIVJHJ6/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-10T22:48:13Z","links":{"resolver":"https://pith.science/pith/KXJOK2NQSZOF7Z4E55FZIVJHJ6","bundle":"https://pith.science/pith/KXJOK2NQSZOF7Z4E55FZIVJHJ6/bundle.json","state":"https://pith.science/pith/KXJOK2NQSZOF7Z4E55FZIVJHJ6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KXJOK2NQSZOF7Z4E55FZIVJHJ6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:KXJOK2NQSZOF7Z4E55FZIVJHJ6","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"5d65551aa4eaf58c1e2b96262b0dc29d27dc920982986fc75beb179bdd2a3612","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-29T19:01:32Z","title_canon_sha256":"88c33f02b6dcd467350394c3c92ee254ffa382d087d519c19a4fd5194c5f1292"},"schema_version":"1.0","source":{"id":"1711.11053","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.11053","created_at":"2026-05-18T00:12:09Z"},{"alias_kind":"arxiv_version","alias_value":"1711.11053v2","created_at":"2026-05-18T00:12:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.11053","created_at":"2026-05-18T00:12:09Z"},{"alias_kind":"pith_short_12","alias_value":"KXJOK2NQSZOF","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_16","alias_value":"KXJOK2NQSZOF7Z4E","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_8","alias_value":"KXJOK2NQ","created_at":"2026-05-18T12:31:28Z"}],"graph_snapshots":[{"event_id":"sha256:c155e01a0779b66111e94adb34bca9925b67d13078e2d0972078e99c5fd04b61","target":"graph","created_at":"2026-05-18T00:12:09Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"We propose a framework for general probabilistic multi-step time series regression. Specifically, we exploit the expressiveness and temporal nature of Sequence-to-Sequence Neural Networks (e.g. recurrent and convolutional structures), the nonparametric nature of Quantile Regression and the efficiency of Direct Multi-Horizon Forecasting. A new training scheme, *forking-sequences*, is designed for sequential nets to boost stability and performance. We show that the approach accommodates both temporal and static covariates, learning across multiple related series, shifting seasonality, future pla","authors_text":"Balakrishnan Narayanaswamy, Dhruv Madeka, Kari Torkkola, Ruofeng Wen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-29T19:01:32Z","title":"A Multi-Horizon Quantile Recurrent Forecaster"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.11053","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:953c730412ed2c09096674ce8c43b70757385f235a9d99a519e138aa28554211","target":"record","created_at":"2026-05-18T00:12:09Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"5d65551aa4eaf58c1e2b96262b0dc29d27dc920982986fc75beb179bdd2a3612","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-29T19:01:32Z","title_canon_sha256":"88c33f02b6dcd467350394c3c92ee254ffa382d087d519c19a4fd5194c5f1292"},"schema_version":"1.0","source":{"id":"1711.11053","kind":"arxiv","version":2}},"canonical_sha256":"55d2e569b0965c5fe784ef4b9455274f9e77d1b455b183eb162b23c0ff41d958","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"55d2e569b0965c5fe784ef4b9455274f9e77d1b455b183eb162b23c0ff41d958","first_computed_at":"2026-05-18T00:12:09.079755Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:12:09.079755Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ONt/S4gqcJ/ziHHp2VHHTc4PB/0/EZPd+44IkKvgxFM+HDxGflapOYaj/gAmdwayf+FrS/q3r6AVijdw5xJQDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:12:09.080410Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.11053","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:953c730412ed2c09096674ce8c43b70757385f235a9d99a519e138aa28554211","sha256:c155e01a0779b66111e94adb34bca9925b67d13078e2d0972078e99c5fd04b61"],"state_sha256":"ac4804156d6303aee8ea4babae31e303bdde265b57abdf6a6226d4ce19a6efbb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bFL4Iba+Cwr2JdkIfUA1HvPrHbdyYLtCpxNlzfAOWdM726r9MjOt4DOl7kv3LPhwexBUDT8qHAtgfNuQzr5JAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-10T22:48:13.434382Z","bundle_sha256":"cb2b6e76652421d896b5f4fe9f434e449fe177455b8e9f2164101ef08d677672"}}