A Novel Discrete Time Series Representation With De Bruijn Graphs for Enhanced Forecasting Using TimesNet
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 anal...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11079555/ |
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| author | Mert Onur Cakiroglu Hasan Kurban Elham Buxton Mehmet Dalkilic |
| author_facet | Mert Onur Cakiroglu Hasan Kurban Elham Buxton Mehmet Dalkilic |
| author_sort | Mert Onur Cakiroglu |
| collection | DOAJ |
| description | 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: <uri>https://github.com/KurbanIntelligenceLab/dBGTime-Series-Library</uri> |
| format | Article |
| id | doaj-art-782a9b35d0664ca69dcf493d22613c4d |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-782a9b35d0664ca69dcf493d22613c4d2025-08-20T02:48:16ZengIEEEIEEE Access2169-35362025-01-011312318212319810.1109/ACCESS.2025.358850711079555A Novel Discrete Time Series Representation With De Bruijn Graphs for Enhanced Forecasting Using TimesNetMert Onur Cakiroglu0https://orcid.org/0009-0001-0798-1361Hasan Kurban1https://orcid.org/0000-0003-3142-2866Elham Buxton2https://orcid.org/0000-0001-7774-4604Mehmet Dalkilic3Department of Computer Science, Indiana University Bloomington, Bloomington, IN, USACollege of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarDepartment of Computer Science, University of Illinois Springfield, Springfield, IL, USADepartment of Computer Science, Indiana University Bloomington, Bloomington, IN, USAIn 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: <uri>https://github.com/KurbanIntelligenceLab/dBGTime-Series-Library</uri>https://ieeexplore.ieee.org/document/11079555/Time series analysisDe Bruijn graphTimesNetgraph embeddings |
| spellingShingle | Mert Onur Cakiroglu Hasan Kurban Elham Buxton Mehmet Dalkilic A Novel Discrete Time Series Representation With De Bruijn Graphs for Enhanced Forecasting Using TimesNet IEEE Access Time series analysis De Bruijn graph TimesNet graph embeddings |
| 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 | novel discrete time series representation with de bruijn graphs for enhanced forecasting using timesnet |
| topic | Time series analysis De Bruijn graph TimesNet graph embeddings |
| url | https://ieeexplore.ieee.org/document/11079555/ |
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