Pith. sign in

Paper Citation Record · LEDGER

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series

As of 14 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2608.00513.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2608.00513 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T00:53:56.699362Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

55 of 55 outbound references displayed

  • verified exact3
  • verified fuzzy31
  • unresolved20
  • parse uncertain1
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a324d614-4460-4f79-ab68-13da52c0aa92 · outbound

This paper cites an unresolved cited work.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Unresolved cited work

Reference 1

Resolution
unresolved
raw_fallback, observed 2026-08-05T00:54:10.204733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:52.120973Z digest=sha256:16159ea8b3d0c361d1e218c69465eec6714058736d10a2e1aea72ef7553b1eff

Observation dd242442-ba32-430e-a2ce-45217ee8221b · outbound

This paper cites an unresolved cited work.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Unresolved cited work

Reference 2

Resolution
unresolved
raw_fallback, observed 2026-08-05T00:54:09.922251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:52.174955Z digest=sha256:34f9b0572162d891c23a635aec2e70f53c5ed6b7fbcc2186b7f35782e1cdd11e

Observation 330860ad-30c0-489a-8edc-2777cbf75acd · outbound

This paper cites an unresolved cited work.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Unresolved cited work

Reference 3

Resolution
unresolved
raw_fallback, observed 2026-08-05T00:54:09.584220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:52.235545Z digest=sha256:9afe3525c7429c5c07cbf072e53af679933b8e80837fd08bc2fcba24505ed647

Observation c57f9817-c10d-48bc-abfc-55598244a224 · outbound

This paper cites an unresolved cited work.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Unresolved cited work

Reference 4

Resolution
unresolved
raw_fallback, observed 2026-08-05T00:54:09.260018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:52.339875Z digest=sha256:367a568f2bdb0f7387ea406a695e22e0f232f5c30b45bb31120fb69cdb4893a5

Observation acd9b673-cacf-4002-ab5f-408f506dbfb3 · outbound

This paper cites an unresolved cited work.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Unresolved cited work

Reference 5

Resolution
unresolved
raw_fallback, observed 2026-08-05T00:54:09.000749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:52.389500Z digest=sha256:48b970accb89a89ed80f957bfb519c890d658f13bc3c4ffe069c2e88b8b02aad

Observation 03a7c430-0a0c-4b99-a850-a5bfc3d7d685 · outbound

This paper cites When an appliance remains in a steady state, the electricity consumption profile on the supply circuit remains relatively stable.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series When an appliance remains in a steady state, the electricity consumption profile on the supply circuit remains relatively stable

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:54:08.681680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:52.439749Z digest=sha256:7e21c8efb6606af1349748b512b33117224f53f6a7d4e41293ac82c17dcf490e

Observation 0647c6b0-b7eb-4585-8921-9c8090a66e74 · outbound

This paper cites an unresolved cited work.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Unresolved cited work

Reference 7

Resolution
unresolved
raw_fallback, observed 2026-08-05T00:54:08.316659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:52.530714Z digest=sha256:ab4339aeeec2a175d42d0b52dd31254cb5120d59cd8424703d0c943a80efb642

Observation 1f1e92c5-80c5-4fb4-bb34-3109752dee59 · outbound

This paper cites The thresholds Δ and ε are defined by inequality (1).

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series The thresholds Δ and ε are defined by inequality (1)

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:54:08.016074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:52.691372Z digest=sha256:dce7b9a3f2bb9c361f163e7a0dc52d6511f42db30c3239ae602725dc16e95d55

Observation 7296a823-392d-4297-b2e4-ef5eb016dac1 · outbound

This paper cites an unresolved cited work.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Unresolved cited work

Reference 9

Resolution
parse uncertain
raw_fallback, observed 2026-08-05T00:54:07.682054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:52.760270Z digest=sha256:c69712f3e155788f4192447c389a3c3e56ac3461f4757936ccd0c35073aaf7e2

Observation f6d5fbdd-9f13-42b5-8aba-0d8df29c0165 · outbound

This paper cites an unresolved cited work.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Unresolved cited work

Reference 10

Resolution
unresolved
raw_fallback, observed 2026-08-05T00:54:07.399370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:52.819512Z digest=sha256:44a77825b836edec8b0794b0f7f90b8f2cf60da8ef5284a8bea9c0ec54613c9b

Observation 174556b0-9e56-48b9-8990-a29eb4860738 · outbound

This paper cites an unresolved cited work.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Unresolved cited work

Reference 11

Resolution
unresolved
raw_fallback, observed 2026-08-05T00:54:07.162232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:52.875150Z digest=sha256:89ed8495075c78ef46888cc823c63d48d7aa8686457dc3d80064378965889432

Observation e0d76831-27fd-44ab-a922-cc6f2e4a4431 · outbound

This paper cites an unresolved cited work.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Unresolved cited work

Reference 12

Resolution
unresolved
raw_fallback, observed 2026-08-05T00:54:06.949580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:52.913201Z digest=sha256:2b6a2a603e47cb8d50c71f76eb44db446fd848951a5427a110ffbe88c385b980

Observation e63a9b7c-36db-40ef-865e-9ec86d940c98 · outbound

This paper cites A novel segmentation approach for work mode boundary detection in MFR pulse sequence[J].

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series A novel segmentation approach for work mode boundary detection in MFR pulse sequence[J]

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:54:01.207385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:54.910998Z digest=sha256:9b8acf2b60fd2fb51cec7e29dd2154e4296ab5f639edf6671a751eb5e83e694f

Observation dafaa72a-3709-4746-a2e5-b312ec46ec11 · outbound

This paper cites an unresolved cited work.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Unresolved cited work

Reference 14

Resolution
unresolved
raw_fallback, observed 2026-08-05T00:54:06.476898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:53.022390Z digest=sha256:6624a75f8f4e6e31df1e1fb6126affcaefa3fec821bb479ded52a4d898d29d05

Observation eef20231-18a5-4563-973f-1e3dada82049 · outbound

This paper cites an unresolved cited work.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Unresolved cited work

Reference 15

Resolution
unresolved
raw_fallback, observed 2026-08-05T00:54:06.224565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:53.110686Z digest=sha256:649c0705c3c371389852b7b124499382b4c99c4d44ce5101b089550a6224b30d

Observation 5efd6f47-b639-45fc-817b-f0556aecb1d1 · outbound

This paper cites an unresolved cited work.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Unresolved cited work

Reference 16

Resolution
unresolved
raw_fallback, observed 2026-08-05T00:54:05.951721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:53.166941Z digest=sha256:834bee20c6d82eacc68831c1056a5a7d30e3c319d86217d18c0d131fe2892ae6

Observation 3d662c3c-b605-4d69-aa02-0b4433b1f882 · outbound

This paper cites an unresolved cited work.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Unresolved cited work

Reference 17

Resolution
unresolved
raw_fallback, observed 2026-08-05T00:54:05.761274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:53.316014Z digest=sha256:2ea6fcbd078b709814fd7e235f4ce66239ae19753532dc15c41f676b47fd2ed5

Observation 696bdb06-6838-40ff-8d46-969c43f2ba11 · outbound

This paper cites an unresolved cited work.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Unresolved cited work

Reference 18

Resolution
unresolved
raw_fallback, observed 2026-08-05T00:54:05.492676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:53.400146Z digest=sha256:c45d853b85a30cca2996d568c1a440965ed24f1edaf43ade55a7294f711d8039

Observation 3c2de50b-376f-4757-ad2c-7d250019ef8b · outbound

This paper cites steady_segments are the output of Algorithm 2.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series steady_segments are the output of Algorithm 2

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:54:06.715940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:52.964617Z digest=sha256:bd4530192dfc047f056db6df91a06f4556c599ea6dfd47ef02d3e0edc759ddbc

Observation 97b6f5fb-b166-429e-9d1e-af9ad0f992a2 · outbound

This paper cites an unresolved cited work.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Unresolved cited work

Reference 20

Resolution
unresolved
raw_fallback, observed 2026-08-05T00:54:05.200692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:53.554132Z digest=sha256:56fe2eb5b0b3660477a5c73727c6cd67ee88dc0591fa81c0ed6d0180c4815c88

Observation 972a13f2-4c72-48e7-a2ff-cd11e99bc70a · outbound

This paper cites an unresolved cited work.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Unresolved cited work

Reference 21

Resolution
unresolved
raw_fallback, observed 2026-08-05T00:54:04.973535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:53.648139Z digest=sha256:2e19494418a4a11ffdb23c1d362b12cdd3a455c46794f655a050d38d92a0ea5e

Observation 41f1a2db-a93f-4f9d-a7ad-d1560dc614d9 · outbound

This paper cites Let 0 , ntt R ∈ with 0 0, 0ntt >> and 0 ntt <.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Let 0 , ntt R ∈ with 0 0, 0ntt >> and 0 ntt <

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:54:04.729155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:53.714240Z digest=sha256:16399c9e502f1e1bf11790b176fa91711e81cf52812bb2f017299339006bdc36

Observation 2047b692-66cd-4f27-9d79-97ec2b859baa · outbound

This paper cites As shown in Figure 2, BayesSeg comprises a segmentation module, an evaluation module, and a parameter -optimization module.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series As shown in Figure 2, BayesSeg comprises a segmentation module, an evaluation module, and a parameter -optimization module

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:54:04.449223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:53.763705Z digest=sha256:d8a85e47280e699271181a563d35db9ec1d89a75741cf15b319ed81620b044f7

Observation 99205b04-b81f-495c-ae40-9b7876ce410c · outbound

This paper cites an unresolved cited work.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Unresolved cited work

Reference 24

Resolution
verified exact
raw_fallback, observed 2026-08-05T00:53:57.581048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:53.818303Z digest=sha256:925396418dbc6080709b38309ff575c9d622f2389382a4d58547d339e3f7d802

Observation 6f05ed84-36cf-4a39-a25e-0a4a1aa76e42 · outbound

This paper cites making weight decisions for the user.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series making weight decisions for the user

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:54:04.195936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:53.950415Z digest=sha256:a1098e96872d8fd4c8f58f31610491d06122a3af047539fd6cf0640bb5dd39a9

Observation 23a858c5-d3f9-4fb1-bdc5-548f958c3de9 · outbound

This paper cites A., ABID M.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series A., ABID M

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:54:03.899786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:54.011712Z digest=sha256:aa884de2b484daaec13d16111c6a6e3505828159656453c68f8523486bca041b

Observation 64b1eafe-5788-476f-8302-a922d611b756 · outbound

This paper cites A Survey of the Research on Non-intrusive Load Monitoring and Disaggregation[J].

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series A Survey of the Research on Non-intrusive Load Monitoring and Disaggregation[J]

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:54:03.640919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:54.093558Z digest=sha256:729d22a613054afbdfe8c01eeafb8c537e05c2a378bde69889867b9a46eee766

Observation 101a1d7b-ea3a-490a-8575-a9e908c16208 · outbound

This paper cites Non -intrusive load monitoring: A systematic review of methods, scenario- specific challenges, and pathways to practical deployment[J].

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Non -intrusive load monitoring: A systematic review of methods, scenario- specific challenges, and pathways to practical deployment[J]

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:54:03.344342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:54.165792Z digest=sha256:bc5831e27255850ef60747aa73d01e5e39672abe12a19947f11a442f0e45c8ce

Observation ef991feb-c054-4f39-96d1-9e71f2c1f166 · outbound

This paper cites Research on Feature Model and Mining Method for Current Transition Sequence[J].

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Research on Feature Model and Mining Method for Current Transition Sequence[J]

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:54:03.089475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:54.256646Z digest=sha256:b23104a9e6c88a438f128600e8692f170e4d320d98f92debe2bf49a42ee82125

Observation c786f34c-3026-482f-bb70-df9f9a4bb1aa · outbound

This paper cites A low -frequency residential NILM approach based on adaptive event detection[J].

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series A low -frequency residential NILM approach based on adaptive event detection[J]

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:54:02.798815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:54.317190Z digest=sha256:d44c51f71ec3e226c044963c5fa7e82f6d9ef2e4678753d7a65d24afdac6c0d5

Observation 83d5eab4-4894-430c-8df0-5fff55c41ac2 · outbound

This paper cites Unsupervised time series segmentation: A survey on recent advances[J].

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Unsupervised time series segmentation: A survey on recent advances[J]

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:54:02.627118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:54.351663Z digest=sha256:7446b8d4a34cc7c073553481eedcb05f3aafaf62320fdbcc84e1952caa75a1db

Observation ddc5608a-77a2-42f3-ac9c-3cb100879c7d · outbound

This paper cites Adaptive algorithms for change point detection in financial time series[J].

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Adaptive algorithms for change point detection in financial time series[J]

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:54:02.383770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:54.408165Z digest=sha256:d4a89effbcc319e3fdbbdac57708aa45c2d7aceeca0cdef1dc68292dbb04601c

Observation efff6abe-9966-4f78-add6-67db853a86c8 · outbound

This paper cites an unresolved cited work.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Unresolved cited work

Reference 33

Resolution
unresolved
raw_fallback, observed 2026-08-05T00:54:02.173585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:54.501519Z digest=sha256:d5ebaa113e31c41531d04dc0c884e7208b2c492f052358ec929e08e7b6f67c67

Observation 34464876-255c-43ff-aced-347fbb3fed5f · outbound

This paper cites CLaP -- State Detection from Time Series.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series CLaP -- State Detection from Time Series

Reference 34

Resolution
verified exact
local_arxiv, observed 2026-08-05T00:53:57.225007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:54.560871Z digest=sha256:5248629ec99c95a28a7d7acc52ab4dc8f80a404fa8373c39c49130282a332528

Observation 4a249aee-48e4-4caf-bd44-0fa411b69b37 · outbound

This paper cites an unresolved cited work.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Unresolved cited work

Reference 35

Resolution
unresolved
raw_fallback, observed 2026-08-05T00:54:01.781493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:54.673422Z digest=sha256:9ec6c73f89a76137aa04b923cb5f801e3937f5c9d71225233508ae909bc125ac

Observation 0e6ed634-70c0-4988-bae7-0db3cf672c60 · outbound

This paper cites an unresolved cited work.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Unresolved cited work

Reference 36

Resolution
unresolved
raw_fallback, observed 2026-08-05T00:54:01.622833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:54.730123Z digest=sha256:62dba38bd3e7288b81f8f7a0c2c4cc9ea74ba72a0ffd1c28264b55f13e258eff

Observation 0adec100-8dd5-4ca7-aad0-1c160cb619c3 · outbound

This paper cites Time2State: An unsupervised framework for inferring the latent states in time series data[J].

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Time2State: An unsupervised framework for inferring the latent states in time series data[J]

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:54:01.404755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:54.855172Z digest=sha256:6e22d55a09b6f3bb0fba63866fd701a65b8dabcced9686ccf689090f2283371e

Observation 0e9c24cf-f3c8-4457-9c51-13fa91e473f6 · outbound

This paper cites Analyzing the performance of biomedical time-series segmentation with electrophysiology data[J].

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Analyzing the performance of biomedical time-series segmentation with electrophysiology data[J]

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:54:01.053838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:54.949485Z digest=sha256:4b9c769caf232270e8adba5cf9e7edfdeb0ad53a609d7564974d44cbc3e27888

Observation 0db23c07-9762-4846-a2cb-3ff0ab5d1bad · outbound

This paper cites A residential labeled dataset for smart meter data analy tics[J].

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series A residential labeled dataset for smart meter data analy tics[J]

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:54:00.748088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:55.008415Z digest=sha256:4b373bd61d891ccb60b424f95976475a92ce5bbb77e65c97f18d933ea8987d61

Observation 13553774-8ad4-4b79-af3c-48c4b8d6ffb6 · outbound

This paper cites Transient event detection algorithm for non-intrusive load monitoring[J].

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Transient event detection algorithm for non-intrusive load monitoring[J]

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:54:00.497572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:55.049002Z digest=sha256:6388ff74a06fb60de424e1cfbde7f4ff440910691e94edf41ad8e4f47b59b2e2

Observation 22262c26-6465-4e1a-8690-b687a63d8ac0 · outbound

This paper cites Nonintrusive load monitoring (NILM) using a deep learning model with a transformer-based attention mechanism and temporal pooling[J].

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Nonintrusive load monitoring (NILM) using a deep learning model with a transformer-based attention mechanism and temporal pooling[J]

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:54:00.229975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:55.105940Z digest=sha256:95958331489fce2552d6aa35d5541d4e730e84ed8dae67aaa8fc15ec5dcee9a1

Observation 3e174615-2c37-4631-bffe-896c82bb1205 · outbound

This paper cites Enhancing non-intrusive load monitoring through transfer learning with transformer models[J].

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Enhancing non-intrusive load monitoring through transfer learning with transformer models[J]

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:53:59.939598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:55.244839Z digest=sha256:095d525161ed80fdfb041075d60067ae6f2e2d989646195eef1e7a962c032496

Observation 56ce09fa-7db9-4bab-adbf-77f2f6eefc34 · outbound

This paper cites Non-intrusive load monitoring model based on SimCLR and visualized color V-I trajectories[J].

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Non-intrusive load monitoring model based on SimCLR and visualized color V-I trajectories[J]

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:53:59.678932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:55.378108Z digest=sha256:5bc3f8bc8e631058ca2039145efcf3c823b275fedf3cb1af9f6a6c113a9d6210

Observation eff08559-3189-471e-bf9d-4593d8257a8f · outbound

This paper cites N., et a l.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series N., et a l

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:53:59.416981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:55.508348Z digest=sha256:1732ecf50e1bebd21d9a0015fc27c12d2593a5429859da848bea35848a8a0f32

Observation 771ef5a6-b190-4d23-bd5c-922f9f667492 · outbound

This paper cites Non-intrusive load monitoring based on time-enhanced multidimensional feature visualization[J].

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Non-intrusive load monitoring based on time-enhanced multidimensional feature visualization[J]

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:53:59.258538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:55.646520Z digest=sha256:07338e9e6d2d884df3d68f42ae675ad1bf3d06087831e6c03c2854142e3a9caa

Observation 0b1122a9-5f94-43b9-aaea-84f3e77213bf · outbound

This paper cites Change -point detection with deep learning: A review[J].

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Change -point detection with deep learning: A review[J]

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:53:59.099901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:55.796933Z digest=sha256:2b1c7de5dbec108e45fa35b315a9854ce67aa08f97f8aec0a5c639fcf212e3e0

Observation d9ebd69a-292d-4c87-8d13-50fdfbf7976d · outbound

This paper cites Automatic change -point detection in time series via deep learning[J].

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Automatic change -point detection in time series via deep learning[J]

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:53:58.919445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:55.901617Z digest=sha256:f495e780ea9fe3cfdde4bb8fca18c0b6f261dee70aa68ca04649bd67c67d38a5

Observation 03fa9fbe-bdd1-42c5-89a7-f8ded1bc48fc · outbound

This paper cites Online neural ne tworks for change-point detection[J].

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Online neural ne tworks for change-point detection[J]

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:53:58.705931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:55.961532Z digest=sha256:997d88bb354153c2c521273a3641aba92463b11ae836027596b38222b8f89fd7

Observation eea281fb-50e2-49f4-a2a8-6448d37333a1 · outbound

This paper cites Short-term power load forecasting based on Seq2Seq model integrating Bayesian optimization, temporal convolutional network and attention[J].

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Short-term power load forecasting based on Seq2Seq model integrating Bayesian optimization, temporal convolutional network and attention[J]

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:53:58.483663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:56.093198Z digest=sha256:a100ac39f39600e37cf5d0e231ea39167d7eb05a4dbc65b4fc6662dd04e6d39a

Observation cbbcdea0-4921-416b-bd37-8f88ca99a09f · outbound

This paper cites A hybrid neural network based on Bayesian optimization for non-intrusive load disaggregation[C].

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series A hybrid neural network based on Bayesian optimization for non-intrusive load disaggregation[C]

Reference 51

Resolution
verified exact
raw_fallback, observed 2026-08-05T00:53:56.988538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:56.191169Z digest=sha256:c47f6a2eb066b1d17fa51a19e1c9c1f1adefb4d3de75271db8b69a8b8332bd1e

Observation aa7e05cc-9425-4088-983d-bb21d6e421bf · outbound

This paper cites R., KHALID S., et al.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series R., KHALID S., et al

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:53:58.283223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:56.341242Z digest=sha256:a11184a2aa1bd04838cd1b44c7bae2ea080fedfe24dabbaa4ef08eb19f44436f

Observation 83b965b4-b5a2-4831-a636-4c5ab44401a6 · outbound

This paper cites Evaluation metrics and statistical tests for machine learning[J].

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Evaluation metrics and statistical tests for machine learning[J]

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:53:58.160582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:56.489628Z digest=sha256:a235b766e98711b73cf46349221ea60ab5fcaba8276a5f3c06c02f7a012e1ccb

Observation 3a76b229-7824-4bb6-a249-4a9df2a799f3 · outbound

This paper cites A closer look at classification evaluation metrics and a critical reflection of common evaluation practice[J].

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series A closer look at classification evaluation metrics and a critical reflection of common evaluation practice[J]

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:53:57.992275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:56.582956Z digest=sha256:ba0a719f3a3417996822f675e91b2eec8bbb4b70b7e207a7e12a89a4684e0fae

Observation 4d8ada95-e42c-4f07-a00a-82d2ebe3209c · outbound

This paper cites An experimental evaluation of anomaly detection in time series[J].

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series An experimental evaluation of anomaly detection in time series[J]

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T00:53:57.747874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:56.699362Z digest=sha256:6d3f749314dd88252e0d0711120874fd0809ec102850039f56a6ec11fc9939a8

Observation a863a53c-59c3-4343-bb6d-aa8ca3b18cad · outbound

This paper cites an unresolved cited work.

BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series Unresolved cited work

Reference 2025

Resolution
unresolved
raw_fallback, observed 2026-08-05T00:54:01.916409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:53:54.599607Z digest=sha256:7994365f1c7616f9070c037494b3a9730bc868800acdc4defbbaf72d35313ca8

Pith citing papers

No inbound Pith citation observations are available.