Long input windows are required to identify the generative process in time series forecasting even for short-memory processes, and decoupling identification from forecasting improves scalability.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
StateFlow extends VARNN with dual hidden and residual-memory states plus a chunk decoder and two-stage training to enable competitive long-horizon time series forecasting while retaining a compact recurrent design.
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Why Do Time Series Models Need Long Context Windows?
Long input windows are required to identify the generative process in time series forecasting even for short-memory processes, and decoupling identification from forecasting improves scalability.
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StateFlow: Dual-State Recurrent Modeling for Long-Horizon Time Series Forecasting
StateFlow extends VARNN with dual hidden and residual-memory states plus a chunk decoder and two-stage training to enable competitive long-horizon time series forecasting while retaining a compact recurrent design.