{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:M7AYKBX746Q3W2JUH4NFHFDVF2","short_pith_number":"pith:M7AYKBX7","canonical_record":{"source":{"id":"1709.05548","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2017-09-16T18:51:58Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"0ec760a032afc1052491a17d8c16a8e27f2947112d3f52882c2c21a87ea67cfd","abstract_canon_sha256":"21084e58074df1e46219a18368f58ca60a44527ab7d6400afe51fbd76cfb248b"},"schema_version":"1.0"},"canonical_sha256":"67c18506ffe7a1bb69343f1a5394752ebf55f6511d8fc8373358a46d49800609","source":{"kind":"arxiv","id":"1709.05548","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.05548","created_at":"2026-05-18T00:35:00Z"},{"alias_kind":"arxiv_version","alias_value":"1709.05548v1","created_at":"2026-05-18T00:35:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.05548","created_at":"2026-05-18T00:35:00Z"},{"alias_kind":"pith_short_12","alias_value":"M7AYKBX746Q3","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_16","alias_value":"M7AYKBX746Q3W2JU","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_8","alias_value":"M7AYKBX7","created_at":"2026-05-18T12:31:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:M7AYKBX746Q3W2JUH4NFHFDVF2","target":"record","payload":{"canonical_record":{"source":{"id":"1709.05548","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2017-09-16T18:51:58Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"0ec760a032afc1052491a17d8c16a8e27f2947112d3f52882c2c21a87ea67cfd","abstract_canon_sha256":"21084e58074df1e46219a18368f58ca60a44527ab7d6400afe51fbd76cfb248b"},"schema_version":"1.0"},"canonical_sha256":"67c18506ffe7a1bb69343f1a5394752ebf55f6511d8fc8373358a46d49800609","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:35:00.343732Z","signature_b64":"ki8iCjyctqmzoN72A6JXczgMKHjG5IPkTs9syr0eyZ2L1G90a06pm/O1xo7XFLxVxBWI2qgllTwPG3IBCGuaBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"67c18506ffe7a1bb69343f1a5394752ebf55f6511d8fc8373358a46d49800609","last_reissued_at":"2026-05-18T00:35:00.342856Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:35:00.342856Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1709.05548","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:35:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"orCxjvVPL9MkthyFOoj9kqBzYKFEAiVQibFDN3khB3A4klIuMCNRIykrJCE/lzj0id1/q+F492YWRsCwqgDGDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-22T08:46:32.284249Z"},"content_sha256":"e18e9dec535b61a6fcd81c34c49e042db11662922ad5fe3734807f2863b70ad1","schema_version":"1.0","event_id":"sha256:e18e9dec535b61a6fcd81c34c49e042db11662922ad5fe3734807f2863b70ad1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:M7AYKBX746Q3W2JUH4NFHFDVF2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Forecasting of commercial sales with large scale Gaussian Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.AP","authors_text":"Evgeny Burnaev, Rodrigo Rivera","submitted_at":"2017-09-16T18:51:58Z","abstract_excerpt":"This paper argues that there has not been enough discussion in the field of applications of Gaussian Process for the fast moving consumer goods industry. Yet, this technique can be important as it e.g., can provide automatic feature relevance determination and the posterior mean can unlock insights on the data. Significant challenges are the large size and high dimensionality of commercial data at a point of sale. The study reviews approaches in the Gaussian Processes modeling for large data sets, evaluates their performance on commercial sales and shows value of this type of models as a decis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.05548","kind":"arxiv","version":1},"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":"2026-05-18T00:35:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mAG1fNFtO/fjYyh1rE7xEQTR/hwCKKlQaSBw6iDmYuPF9CpA0xuy7/CyBTad3xnVtNby5T+PiyGqmC4O9xe9CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-22T08:46:32.284612Z"},"content_sha256":"20f66b5362384cb9e9d1c9b70cf22f76f087f6656784e157fd2e291e7d6ab819","schema_version":"1.0","event_id":"sha256:20f66b5362384cb9e9d1c9b70cf22f76f087f6656784e157fd2e291e7d6ab819"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/M7AYKBX746Q3W2JUH4NFHFDVF2/bundle.json","state_url":"https://pith.science/pith/M7AYKBX746Q3W2JUH4NFHFDVF2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/M7AYKBX746Q3W2JUH4NFHFDVF2/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":"2026-06-22T08:46:32Z","links":{"resolver":"https://pith.science/pith/M7AYKBX746Q3W2JUH4NFHFDVF2","bundle":"https://pith.science/pith/M7AYKBX746Q3W2JUH4NFHFDVF2/bundle.json","state":"https://pith.science/pith/M7AYKBX746Q3W2JUH4NFHFDVF2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/M7AYKBX746Q3W2JUH4NFHFDVF2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:M7AYKBX746Q3W2JUH4NFHFDVF2","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":"21084e58074df1e46219a18368f58ca60a44527ab7d6400afe51fbd76cfb248b","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2017-09-16T18:51:58Z","title_canon_sha256":"0ec760a032afc1052491a17d8c16a8e27f2947112d3f52882c2c21a87ea67cfd"},"schema_version":"1.0","source":{"id":"1709.05548","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.05548","created_at":"2026-05-18T00:35:00Z"},{"alias_kind":"arxiv_version","alias_value":"1709.05548v1","created_at":"2026-05-18T00:35:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.05548","created_at":"2026-05-18T00:35:00Z"},{"alias_kind":"pith_short_12","alias_value":"M7AYKBX746Q3","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_16","alias_value":"M7AYKBX746Q3W2JU","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_8","alias_value":"M7AYKBX7","created_at":"2026-05-18T12:31:31Z"}],"graph_snapshots":[{"event_id":"sha256:20f66b5362384cb9e9d1c9b70cf22f76f087f6656784e157fd2e291e7d6ab819","target":"graph","created_at":"2026-05-18T00:35:00Z","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":"This paper argues that there has not been enough discussion in the field of applications of Gaussian Process for the fast moving consumer goods industry. Yet, this technique can be important as it e.g., can provide automatic feature relevance determination and the posterior mean can unlock insights on the data. Significant challenges are the large size and high dimensionality of commercial data at a point of sale. The study reviews approaches in the Gaussian Processes modeling for large data sets, evaluates their performance on commercial sales and shows value of this type of models as a decis","authors_text":"Evgeny Burnaev, Rodrigo Rivera","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2017-09-16T18:51:58Z","title":"Forecasting of commercial sales with large scale Gaussian Processes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.05548","kind":"arxiv","version":1},"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:e18e9dec535b61a6fcd81c34c49e042db11662922ad5fe3734807f2863b70ad1","target":"record","created_at":"2026-05-18T00:35:00Z","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":"21084e58074df1e46219a18368f58ca60a44527ab7d6400afe51fbd76cfb248b","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2017-09-16T18:51:58Z","title_canon_sha256":"0ec760a032afc1052491a17d8c16a8e27f2947112d3f52882c2c21a87ea67cfd"},"schema_version":"1.0","source":{"id":"1709.05548","kind":"arxiv","version":1}},"canonical_sha256":"67c18506ffe7a1bb69343f1a5394752ebf55f6511d8fc8373358a46d49800609","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"67c18506ffe7a1bb69343f1a5394752ebf55f6511d8fc8373358a46d49800609","first_computed_at":"2026-05-18T00:35:00.342856Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:35:00.342856Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ki8iCjyctqmzoN72A6JXczgMKHjG5IPkTs9syr0eyZ2L1G90a06pm/O1xo7XFLxVxBWI2qgllTwPG3IBCGuaBg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:35:00.343732Z","signed_message":"canonical_sha256_bytes"},"source_id":"1709.05548","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e18e9dec535b61a6fcd81c34c49e042db11662922ad5fe3734807f2863b70ad1","sha256:20f66b5362384cb9e9d1c9b70cf22f76f087f6656784e157fd2e291e7d6ab819"],"state_sha256":"3538010de27ab90805799183a60b6ac64919ba51f1c5f8fdea335733d07597e7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5VlXEW2c3ltbFMJ9qoGiIU6TglZIyGw6fq3NjiuVrV3NXgOyrDZRGVDfDrm1Dsi6hXWzb5PUvi7whQQ7MOQ2CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-22T08:46:32.286757Z","bundle_sha256":"ef15a8024ef42fe369d437f9733135d807b3743ce0c12bdd2858c198efb318d1"}}