A Novel Discrete Time Series Representation With De Bruijn Graphs for Enhanced Forecasting Using TimesNet
<p dir="ltr">In this paper, we present a novel method for advancing time series forecasting by representing discretized time series data through de Bruijn Graphs (dBGs). This method harnesses the capability of dBGs to encapsulate and project future states from historical sequences, t...
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| مؤلفون آخرون: | , , |
| منشور في: |
2025
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| _version_ | 1864513531513143296 |
|---|---|
| author | Mert Onur Cakiroglu (22927777) |
| author2 | Hasan Kurban (13144983) Elham Buxton (22927780) Mehmet Dalkilic (13144986) |
| author2_role | author author author |
| author_facet | Mert Onur Cakiroglu (22927777) Hasan Kurban (13144983) Elham Buxton (22927780) Mehmet Dalkilic (13144986) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mert Onur Cakiroglu (22927777) Hasan Kurban (13144983) Elham Buxton (22927780) Mehmet Dalkilic (13144986) |
| dc.date.none.fl_str_mv | 2025-07-18T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2025.3588507 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_Novel_Discrete_Time_Series_Representation_With_De_Bruijn_Graphs_for_Enhanced_Forecasting_Using_TimesNet/30971014 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Time series analysis De Bruijn graph TimesNet graph embeddings Forecasting Feature extraction Predictive models Data models Transformers Encoding Computational modeling Accuracy Training |
| dc.title.none.fl_str_mv | A Novel Discrete Time Series Representation With De Bruijn Graphs for Enhanced Forecasting Using TimesNet |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">In this paper, we present a novel method for advancing time series forecasting by representing discretized time series data through de Bruijn Graphs (dBGs). This method harnesses the capability of dBGs to encapsulate and project future states from historical sequences, thus enhancing predictive analytics in time series. Our approach is multi-faceted, involving: 1) encoding time series data as a dBG; 2) the application of graph representation learning, specifically struct2vec, to distill salient features from dBG constructed from time series and 3) the seamless integration of these extracted features into the state of the art TimesNet model to bolster short-term forecasting accuracy. Empirical evaluations conducted on the M4 datasets illustrate that our approach not only maintains the intrinsic dynamics of the time series but also achieves notable improvements in forecasting performance across diverse datasets. All the code developed for this study can be found at: <a href="https://github.com/KurbanIntelligenceLab/dBGTime-Series-Library" target="_blank">https://github.com/KurbanIntelligenceLab/dBGTime-Series-Library</a></p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2025.3588507" target="_blank">https://dx.doi.org/10.1109/access.2025.3588507</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_6a9f8a7f0f88b9dc190f2d6e5af099fc |
| identifier_str_mv | 10.1109/access.2025.3588507 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30971014 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Novel Discrete Time Series Representation With De Bruijn Graphs for Enhanced Forecasting Using TimesNetMert Onur Cakiroglu (22927777)Hasan Kurban (13144983)Elham Buxton (22927780)Mehmet Dalkilic (13144986)Information and computing sciencesArtificial intelligenceComputer vision and multimedia computationMachine learningTime series analysisDe Bruijn graphTimesNetgraph embeddingsForecastingFeature extractionPredictive modelsData modelsTransformersEncodingComputational modelingAccuracyTraining<p dir="ltr">In this paper, we present a novel method for advancing time series forecasting by representing discretized time series data through de Bruijn Graphs (dBGs). This method harnesses the capability of dBGs to encapsulate and project future states from historical sequences, thus enhancing predictive analytics in time series. Our approach is multi-faceted, involving: 1) encoding time series data as a dBG; 2) the application of graph representation learning, specifically struct2vec, to distill salient features from dBG constructed from time series and 3) the seamless integration of these extracted features into the state of the art TimesNet model to bolster short-term forecasting accuracy. Empirical evaluations conducted on the M4 datasets illustrate that our approach not only maintains the intrinsic dynamics of the time series but also achieves notable improvements in forecasting performance across diverse datasets. All the code developed for this study can be found at: <a href="https://github.com/KurbanIntelligenceLab/dBGTime-Series-Library" target="_blank">https://github.com/KurbanIntelligenceLab/dBGTime-Series-Library</a></p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2025.3588507" target="_blank">https://dx.doi.org/10.1109/access.2025.3588507</a></p>2025-07-18T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2025.3588507https://figshare.com/articles/journal_contribution/A_Novel_Discrete_Time_Series_Representation_With_De_Bruijn_Graphs_for_Enhanced_Forecasting_Using_TimesNet/30971014CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/309710142025-07-18T12:00:00Z |
| spellingShingle | A Novel Discrete Time Series Representation With De Bruijn Graphs for Enhanced Forecasting Using TimesNet Mert Onur Cakiroglu (22927777) Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Time series analysis De Bruijn graph TimesNet graph embeddings Forecasting Feature extraction Predictive models Data models Transformers Encoding Computational modeling Accuracy Training |
| status_str | publishedVersion |
| title | A Novel Discrete Time Series Representation With De Bruijn Graphs for Enhanced Forecasting Using TimesNet |
| title_full | A Novel Discrete Time Series Representation With De Bruijn Graphs for Enhanced Forecasting Using TimesNet |
| title_fullStr | A Novel Discrete Time Series Representation With De Bruijn Graphs for Enhanced Forecasting Using TimesNet |
| title_full_unstemmed | A Novel Discrete Time Series Representation With De Bruijn Graphs for Enhanced Forecasting Using TimesNet |
| title_short | A Novel Discrete Time Series Representation With De Bruijn Graphs for Enhanced Forecasting Using TimesNet |
| title_sort | A Novel Discrete Time Series Representation With De Bruijn Graphs for Enhanced Forecasting Using TimesNet |
| topic | Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Time series analysis De Bruijn graph TimesNet graph embeddings Forecasting Feature extraction Predictive models Data models Transformers Encoding Computational modeling Accuracy Training |