AverageLinear: Enhance Long-Term Time series forcasting with simple averaging

• URL: http://arxiv.org/abs/2412.20727v1
• Date: Mon, 30 Dec 2024 05:56:25 GMT
• Title: AverageLinear: Enhance Long-Term Time series forcasting with simple averaging
• Authors: Gaoxiang Zhao, Li Zhou, Xiaoqiang Wang,
• Abstract summary: Long-term time series analysis aims to forecast long-term trends by examining changes over past and future periods. Models based on the Transformer architecture, through the application of attention mechanisms, have demonstrated notable performance advantages. Our research reveals that the attention mechanism is not the core component responsible for performance enhancement.
• Score: 6.125620036017928
• License:
• Abstract: Long-term time series analysis aims to forecast long-term trends by examining changes over past and future periods. The intricacy of time series data poses significant challenges for modeling. Models based on the Transformer architecture, through the application of attention mechanisms to channels and sequences, have demonstrated notable performance advantages. In contrast, methods based on convolutional neural networks or linear models often struggle to effectively handle scenarios with large number of channels. However, our research reveals that the attention mechanism is not the core component responsible for performance enhancement. We have designed an exceedingly simple linear structure AverageLinear. By employing straightforward channel embedding and averaging operations, this model can effectively capture correlations between channels while maintaining a lightweight architecture. Experimentss on real-world datasets shows that AverageLinear matches or even surpasses state-of-the-art Transformer-based structures in performance. This indicates that using purely linear structures can also endow models with robust predictive power.

Related papers

• Powerformer: A Transformer with Weighted Causal Attention for Time-series Forecasting [50.298817606660826]
  We introduce Powerformer, a novel Transformer variant that replaces noncausal attention weights with causal weights that are reweighted according to a smooth heavy-tailed decay. Our empirical results demonstrate that Powerformer achieves state-of-the-art accuracy on public time-series benchmarks. Our analyses show that the model's locality bias is amplified during training, demonstrating an interplay between time-series data and power-law-based attention.
  arXiv  Detail & Related papers  (2025-02-10T04:42:11Z)
• Bridging Simplicity and Sophistication using GLinear: A Novel Architecture for Enhanced Time Series Prediction [7.887606414896063]
  Time Series Forecasting (TSF) is an important application across many fields. Recent research suggests simpler linear models might outperform or at least provide competitive performance compared to complex Transformer-based models for TSF tasks. We propose a novel data-efficient architecture, GLinear, for multivariate TSF that exploits periodic patterns to provide better accuracy.
  arXiv  Detail & Related papers  (2025-01-02T06:19:53Z)
• UmambaTSF: A U-shaped Multi-Scale Long-Term Time Series Forecasting Method Using Mamba [7.594115034632109]
  We propose UmambaTSF, a novel long-term time series forecasting framework. It integrates multi-scale feature extraction capabilities of U-shaped encoder-decoder multilayer perceptrons (MLP) with Mamba's long sequence representation. UmambaTSF achieves state-of-the-art performance and excellent generality on widely used benchmark datasets.
  arXiv  Detail & Related papers  (2024-10-15T04:56:43Z)
• sTransformer: A Modular Approach for Extracting Inter-Sequential and Temporal Information for Time-Series Forecasting [6.434378359932152]
  We review and categorize existing Transformer-based models into two main types: (1) modifications to the model structure and (2) modifications to the input data. We propose $textbfsTransformer$, which introduces the Sequence and Temporal Convolutional Network (STCN) to fully capture both sequential and temporal information. We compare our model with linear models and existing forecasting models on long-term time-series forecasting, achieving new state-of-the-art results.
  arXiv  Detail & Related papers  (2024-08-19T06:23:41Z)
• Rough Transformers for Continuous and Efficient Time-Series Modelling [46.58170057001437]
  Time-series data in real-world medical settings typically exhibit long-range dependencies and are observed at non-uniform intervals. We introduce the Rough Transformer, a variation of the Transformer model which operates on continuous-time representations of input sequences. We find that Rough Transformers consistently outperform their vanilla attention counterparts while obtaining the benefits of Neural ODE-based models.
  arXiv  Detail & Related papers  (2024-03-15T13:29:45Z)
• Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach [71.67506068703314]
  Long-term urban mobility predictions play a crucial role in the effective management of urban facilities and services. Traditionally, urban mobility data has been structured as videos, treating longitude and latitude as fundamental pixels. In our research, we introduce a fresh perspective on urban mobility prediction. Instead of oversimplifying urban mobility data as traditional video data, we regard it as a complex time series.
  arXiv  Detail & Related papers  (2023-12-04T07:39:05Z)
• Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution [57.71199089609161]
  Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning. Transformer models have been adopted to deliver high prediction capacity because of the high computational self-attention mechanism. We propose an efficient Transformerbased model, named Conformer, which differentiates itself from existing methods for LTTF in three aspects.
  arXiv  Detail & Related papers  (2023-01-05T13:59:29Z)
• Stacking VAE with Graph Neural Networks for Effective and Interpretable Time Series Anomaly Detection [5.935707085640394]
  We propose a stacking variational auto-encoder (VAE) model with graph neural networks for the effective and interpretable time-series anomaly detection. We show that our proposed model outperforms the strong baselines on three public datasets with considerable improvements.
  arXiv  Detail & Related papers  (2021-05-18T09:50:00Z)
• DAIS: Automatic Channel Pruning via Differentiable Annealing Indicator Search [55.164053971213576]
  convolutional neural network has achieved great success in fulfilling computer vision tasks despite large computation overhead. Structured (channel) pruning is usually applied to reduce the model redundancy while preserving the network structure. Existing structured pruning methods require hand-crafted rules which may lead to tremendous pruning space.
  arXiv  Detail & Related papers  (2020-11-04T07:43:01Z)
• Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems [65.44033635330604]
  We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system. We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony. Results are presented for a test case using load data from an electrical grid.
  arXiv  Detail & Related papers  (2020-10-08T20:25:52Z)
• Liquid Time-constant Networks [117.57116214802504]
  We introduce a new class of time-continuous recurrent neural network models. Instead of declaring a learning system's dynamics by implicit nonlinearities, we construct networks of linear first-order dynamical systems. These neural networks exhibit stable and bounded behavior, yield superior expressivity within the family of neural ordinary differential equations.
  arXiv  Detail & Related papers  (2020-06-08T09:53:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.