AI RESEARCH
FRWKV+: Adaptive Periodic-Position Branch Interaction for Frequency-Space Linear Time Series Forecasting
arXiv CS.LG
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ArXi:2605.15690v1 Announce Type: new Long-term time series forecasting is essential for decision making in energy, finance, transportation, and healthcare systems. Recent lightweight forecasting models improve efficiency by operating in transformed or linearized spaces, but two challenges remain in frequency-space forecasting. The real and imaginary streams of complex spectra contain complementary information that is often weakly exchanged, and periodic-position cues can help recurring patterns only when they are reliable for the current dataset and prediction horizon.