# Disentangled Parameter-Efficient Linear Model for Long-Term Time Series Forecasting

arXiv: https://arxiv.org/abs/2411.17257 · HTML: https://mlsanity.com/paper/2411.17257/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 26 references that are in the map.
- First posted on arXiv 26 Nov 2024.

## Builds on (most cited first, 21 of 21)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [Reformer: The Efficient Transformer](https://mlsanity.com/paper/2001.04451/) (2020)
- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting](https://mlsanity.com/paper/2201.12740/) (2022)
- [TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis](https://mlsanity.com/paper/2210.02186/) (2022)
- [iTransformer: Inverted Transformers Are Effective for Time Series Forecasting](https://mlsanity.com/paper/2310.06625/) (2023)
- [Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting](https://mlsanity.com/paper/1907.00235/) (2019)
- [Dynamic Convolution: Attention over Convolution Kernels](https://mlsanity.com/paper/1912.03458/) (2019)
- [FITS: Modeling Time Series with 10k Parameters](https://mlsanity.com/paper/2307.03756/) (2023)
- [Frequency-domain MLPs are More Effective Learners in Time Series Forecasting](https://mlsanity.com/paper/2311.06184/) (2023)
- [TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis](https://mlsanity.com/paper/2410.16032/) (2024)
- [SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters](https://mlsanity.com/paper/2405.00946/) (2024)
- [FreDF: Learning to Forecast in the Frequency Domain](https://mlsanity.com/paper/2402.02399/) (2024)
- [An Analysis of Linear Time Series Forecasting Models](https://mlsanity.com/paper/2403.14587/) (2024)
- [Mixture-of-Linear-Experts for Long-term Time Series Forecasting](https://mlsanity.com/paper/2312.06786/) (2023)
- [Mlinear: Rethink the Linear Model for Time-series Forecasting](https://mlsanity.com/paper/2305.04800/) (2023)
- [FTMixer: Frequency and Time Domain Representations Fusion for Time Series Modeling](https://mlsanity.com/paper/2405.15256/) (2024)
- [Is Channel Independent strategy optimal for Time Series Forecasting?](https://mlsanity.com/paper/2310.17658/) (2023)

## Built on by (most cited first, 4 of 4)

- [ProtoTS: Learning Hierarchical Prototypes for Explainable Time Series Forecasting](https://mlsanity.com/paper/2509.23159/) (2025)
- [Naga: Vedic Encoding for Deep State Space Models](https://mlsanity.com/paper/2511.13510/) (2025)
- [AI-Driven Multiscenario Interest Rate Forecasting: A Proof of Concept for Banking Asset Management](https://mlsanity.com/paper/2608.12424/) (2026)
- [From HL to H+L-1 Parameters: A Hankel-Toeplitz Forecaster for Long-Term Time Series Forecasting](https://mlsanity.com/paper/2609.33984/) (2026)

## Abstract (arXiv)

Long-term Time Series Forecasting (LTSF) is crucial across various domains, but complex deep models like Transformers are often prone to overfitting on extended sequences. Linear Fully Connected models have emerged as a powerful alternative, achieving competitive results with fewer parameters. However, their reliance on a single, monolithic weight matrix leads to quadratic parameter redundancy and an entanglement of temporal and frequential properties. To address this, we propose DiPE-Linear, a novel model that disentangles this monolithic mapping into a sequence of specialized, parameter-efficient modules. DiPE-Linear features three core components: Static Frequential Attention to prioritize critical frequencies, Static Time Attention to focus on key time steps, and Independent Frequential Mapping to independently process frequency components. A Low-rank Weight Sharing policy further enhances efficiency for multivariate data. This disentangled architecture collectively reduces parameter complexity from quadratic to linear and computational complexity to log-linear. Experiments on real-world datasets show that DiPE-Linear delivers state-of-the-art performance with significantly fewer parameters, establishing a new and highly efficient baseline for LTSF. Our code is available at https://github.com/wintertee/DiPE-Linear/