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    A Novel Discrete Time Series Representation With De Bruijn Graphs for Enhanced Forecasting Using TimesNet by Mert Onur Cakiroglu, Hasan Kurban, Elham Buxton, Mehmet Dalkilic

    Published 2025-01-01
    “…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. …”
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