@inproceedings{b804a5ea3e774179941c9a413a19ebf0,
title = "A Novel Discrete Time Series Representation with De Bruijn Graphs for Enhanced Forecasting Using TimesNet (Extended Abstract)",
abstract = "This paper introduces a novel method for time series forecasting using de Bruijn Graphs (dBGs) to represent discretized time series data. Our approach involves (1) encoding time series as a dBG, (2) applying both novel and existing graph encoding algorithms (like struct2vec) to extract features from dBG, and (3) integrating these features into the TimesNet model to enhance short-term univariate forecasting accuracy. Empirical results on the M4 datasets show that our method preserves the dynamics of the time series while improving forecasting performance across various datasets.",
keywords = "De Bruijn graph, Graph embeddings, Time series analysis, Times-Net",
author = "Cakiroglu, \{Mert Onur\} and Hasan Kurban and Buxton, \{Elham Khorasani\} and Mehmet Dalkilic",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 11th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2024 ; Conference date: 06-10-2024 Through 10-10-2024",
year = "2024",
month = oct,
day = "10",
doi = "10.1109/DSAA61799.2024.10722826",
language = "English",
series = "2024 IEEE 11th International Conference on Data Science and Advanced Analytics, DSAA 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2024 IEEE 11th International Conference on Data Science and Advanced Analytics, DSAA 2024",
address = "United States",
}