Presents SE-WaveNet with weight-tied dilated convolutions plus wavelet and spectral components that reproduces empirical scaling collapse on financial returns while using L times fewer convolutional parameters.
Informer: Beyond efficient transformer for long sequence time-series forecasting
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
fields
cs.LG 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
CastFlow introduces a role-specialized agentic workflow with memory retrieval and multi-view toolkit for iterative ensemble time series forecasting, using two-stage SFT+RLVR training on a domain-specific LLM to outperform static baselines.
NoRIN replaces affine reversible normalization with a Johnson S_U non-linear transform whose shape parameters are initialized by quantile fitting and refined by Bayesian optimization on validation data, yielding backbone-dependent optima that differ from the linear limit.
FTimeXer improves power-grid carbon intensity forecasts by combining an FFT frequency branch with gated fusion and stochastic exogenous masking plus consistency regularization, showing gains on three real datasets.
citing papers explorer
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Scale-Equivariant Generative Forecasting: Weight-Tied Dilated Convolutions, Wavelet Scattering Inputs, and Spectral-Consistency Training for Self-Similar Time Series
Presents SE-WaveNet with weight-tied dilated convolutions plus wavelet and spectral components that reproduces empirical scaling collapse on financial returns while using L times fewer convolutional parameters.
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CastFlow: Learning Role-Specialized Agentic Workflows for Time Series Forecasting
CastFlow introduces a role-specialized agentic workflow with memory retrieval and multi-view toolkit for iterative ensemble time series forecasting, using two-stage SFT+RLVR training on a domain-specific LLM to outperform static baselines.
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NoRIN: Backbone-Adaptive Reversible Normalization for Time-Series Forecasting
NoRIN replaces affine reversible normalization with a Johnson S_U non-linear transform whose shape parameters are initialized by quantile fitting and refined by Bayesian optimization on validation data, yielding backbone-dependent optima that differ from the linear limit.
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FTimeXer: Frequency-aware Time-series Transformer with Exogenous variables for Robust Carbon Footprint Forecasting
FTimeXer improves power-grid carbon intensity forecasts by combining an FFT frequency branch with gated fusion and stochastic exogenous masking plus consistency regularization, showing gains on three real datasets.