Adaptive Seasonal-Trend Decomposition for Streaming Time Series Data with Transitions and Fluctuations in Seasonality
ECML-PKDD' 2024
Welcome to the supplementary website for our paper, "Adaptive Seasonal-Trend Decomposition for Streaming Time Series Data with Transitions and Fluctuations in Seasonality," to appear at the ECML-PKDD 2024 conference.
DOI: https://doi.org/10.1007/978-3-031-70344-7_25
Cite: Phungtua-eng, T., Yamamoto, Y. (2024). Adaptive Seasonal-Trend Decomposition for Streaming Time Series Data with Transitions and Fluctuations in Seasonality. In: Bifet, A., Davis, J., Krilavičius, T., Kull, M., Ntoutsi, E., Žliobaitė, I. (eds) Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2024. Lecture Notes in Computer Science(), vol 14942. Springer, Cham. https://doi.org/10.1007/978-3-031-70344-7_25