AI RESEARCH
TSNN: A Non-parametric and Interpretable Framework for Traffic Time Series Forecasting
arXiv CS.LG
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ArXi:2605.09208v1 Announce Type: new Although many complex models were proposed to analyze time series data, some studies have nstrated remarkable performance with simpler structures. A recent study proposed a non-parametric framework for 3D point cloud classification, which has the potential to be adapted for time series forecasting and enable interpretability. Inspired by the previous works, we present TSNN, a non-parametric and interpretable framework for traffic time series forecasting.