SPDM is a geometry-aware state-space model that projects covariance matrices onto the SPD manifold tangent space and uses geometric gating to modulate SSM parameters for improved multivariate time series forecasting.
Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
abstract
Although Transformer-based methods have significantly improved state-of-the-art results for long-term series forecasting, they are not only computationally expensive but more importantly, are unable to capture the global view of time series (e.g. overall trend). To address these problems, we propose to combine Transformer with the seasonal-trend decomposition method, in which the decomposition method captures the global profile of time series while Transformers capture more detailed structures. To further enhance the performance of Transformer for long-term prediction, we exploit the fact that most time series tend to have a sparse representation in well-known basis such as Fourier transform, and develop a frequency enhanced Transformer. Besides being more effective, the proposed method, termed as Frequency Enhanced Decomposed Transformer ({\bf FEDformer}), is more efficient than standard Transformer with a linear complexity to the sequence length. Our empirical studies with six benchmark datasets show that compared with state-of-the-art methods, FEDformer can reduce prediction error by $14.8\%$ and $22.6\%$ for multivariate and univariate time series, respectively. Code is publicly available at https://github.com/MAZiqing/FEDformer.
representative citing papers
TOA augments attention with learnable sequence-space operators and stochastic regularization to enable signed temporal mixing, yielding gains on forecasting and related benchmarks when added to PatchTST and iTransformer.
Neural CDEs serve as correctors that reduce error accumulation in multi-step forecasts from learned time-series models across synthetic, physics, and real-world data.
TelecomTS is a de-anonymized 5G observability benchmark with scale information, anomalies, and multi-modal QA tasks, on which current foundation models perform poorly.
RhyMix reaches state-of-the-art long-term multivariate forecasting on 10 of 12 public benchmarks with a ~40K-parameter dual-path adaptive architecture of linear complexity.
StockR1 unifies LLM-based financial reasoning and time-series forecasting by emitting verifiable forecast actions that condition a decoder, optimized via consistency-grounded RL to improve accuracy on QA and prediction tasks.
MSTN claims SOTA across time-series tasks with early temporal pooling, but internal inconsistencies in parameter counts, benchmark tables, and implausible error reductions undermine the central claims.
Signed Dual Attention is a parameter-free attention module that models signed dependencies in time series via dual message passing to achieve two-head expressiveness in one block.
citing papers explorer
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SPDM: Geometry-Modulated State Space Modeling with Manifold Constraints for Time Series Forecasting
SPDM is a geometry-aware state-space model that projects covariance matrices onto the SPD manifold tangent space and uses geometric gating to modulate SSM parameters for improved multivariate time series forecasting.
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Beyond Similarity: Temporal Operator Attention for Time Series Analysis
TOA augments attention with learnable sequence-space operators and stochastic regularization to enable signed temporal mixing, yielding gains on forecasting and related benchmarks when added to PatchTST and iTransformer.
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Neural CDEs as Correctors for Learned Time Series Models
Neural CDEs serve as correctors that reduce error accumulation in multi-step forecasts from learned time-series models across synthetic, physics, and real-world data.
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TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis
TelecomTS is a de-anonymized 5G observability benchmark with scale information, anomalies, and multi-modal QA tasks, on which current foundation models perform poorly.
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RhyMix: A Lightweight Adaptive Multi-Rhythm Network for Long-Term Time Series Forecasting
RhyMix reaches state-of-the-art long-term multivariate forecasting on 10 of 12 public benchmarks with a ~40K-parameter dual-path adaptive architecture of linear complexity.
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Reasoning through Verifiable Forecast Actions: Consistency-Grounded RL for Financial LLMs
StockR1 unifies LLM-based financial reasoning and time-series forecasting by emitting verifiable forecast actions that condition a decoder, optimized via consistency-grounded RL to improve accuracy on QA and prediction tasks.
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MSTN: A Lightweight and Fast Model for General TimeSeries Analysis
MSTN claims SOTA across time-series tasks with early temporal pooling, but internal inconsistencies in parameter counts, benchmark tables, and implausible error reductions undermine the central claims.
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Signed Dual Attention: Capturing Signed Dependencies in Time Series Forecasting
Signed Dual Attention is a parameter-free attention module that models signed dependencies in time series via dual message passing to achieve two-head expressiveness in one block.