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

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.