Super-Linear introduces a pretrained MoE architecture using frequency-specialized linear experts and spectral gating for efficient general time series forecasting.
Sparsetsf: Modeling long-term time series forecasting with 1k parameters
5 Pith papers cite this work. Polarity classification is still indexing.
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Optimized Ridge regression with series-specific preprocessing beats prior linear forecasters and exceeds Transformer, MLP, and CNN baselines on six of eight time-series benchmarks.
Characteristic roots govern dynamics in linear forecasting models but noise induces spurious roots; rank reduction and Root Purge regularization mitigate this for more robust predictions.
A two-stage residual-aware framework adds a meta-corrector after a base transformer to model structured errors and reports state-of-the-art results on eight time-series benchmarks.
PPM injects parametric structural priors into generative models via a learnable mapping to improve probabilistic forecasts on non-stationary MTS data.
citing papers explorer
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Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting
Super-Linear introduces a pretrained MoE architecture using frequency-specialized linear experts and spectral gating for efficient general time series forecasting.
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How Good Can Linear Models Be for Time-Series Forecasting?
Optimized Ridge regression with series-specific preprocessing beats prior linear forecasters and exceeds Transformer, MLP, and CNN baselines on six of eight time-series benchmarks.
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Characteristic Root Analysis and Regularization for Linear Time Series Forecasting
Characteristic roots govern dynamics in linear forecasting models but noise induces spurious roots; rank reduction and Root Purge regularization mitigate this for more robust predictions.
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One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data
A two-stage residual-aware framework adds a meta-corrector after a base transformer to model structured errors and reports state-of-the-art results on eight time-series benchmarks.
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Parametric Prior Mapping Framework for Non-stationary Probabilistic Time Series Forecasting
PPM injects parametric structural priors into generative models via a learnable mapping to improve probabilistic forecasts on non-stationary MTS data.