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SEED: An Effective Model for Highly-Skewed Streamflow Time Series Data Forecasting | IEEE Conference Publication | IEEE Xplore

SEED: An Effective Model for Highly-Skewed Streamflow Time Series Data Forecasting


Abstract:

Accurate time series forecasting is crucial in various domains, but predicting highly-skewed and heavy-tailed univariate series poses challenges. We introduce the Segment...Show More

Abstract:

Accurate time series forecasting is crucial in various domains, but predicting highly-skewed and heavy-tailed univariate series poses challenges. We introduce the Segment-Expandable Encoder-Decoder (SEED) model, designed for such time series. SEED incorporates segment representation learning, Kullback-Leibler divergence regularization, and an importance-enhanced sampling policy. We tested our model on the 3-day ahead single-shot prediction task on four hydrologic datasets. Experimental results demonstrate SEED’s effectiveness in optimizing the forecasting process (10-30% of root mean square error reductions over state-of-the-art methods), underlining its notable potential for practical applications in univariate, skewed, long-term time series prediction tasks.
Date of Conference: 15-18 December 2023
Date Added to IEEE Xplore: 22 January 2024
ISBN Information:
Conference Location: Sorrento, Italy

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