PDMLP: Patch-based Decomposed MLP for Long-Term Time Series Forecasting

• URL: http://arxiv.org/abs/2405.13575v2
• Date: Tue, 28 May 2024 02:14:18 GMT
• Title: PDMLP: Patch-based Decomposed MLP for Long-Term Time Series Forecasting
• Authors: Peiwang Tang, Weitai Zhang,
• Abstract summary: Recent studies have attempted to refine the Transformer architecture to demonstrate its effectiveness in Long-Term Time Series Forecasting (LTSF) tasks. We attribute the effectiveness of these models largely to the adopted Patch mechanism, which enhances sequence locality. Further investigation suggests that simple linear layers augmented with the Patch mechanism may outperform complex Transformer-based LTSF models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent studies have attempted to refine the Transformer architecture to demonstrate its effectiveness in Long-Term Time Series Forecasting (LTSF) tasks. Despite surpassing many linear forecasting models with ever-improving performance, we remain skeptical of Transformers as a solution for LTSF. We attribute the effectiveness of these models largely to the adopted Patch mechanism, which enhances sequence locality to an extent yet fails to fully address the loss of temporal information inherent to the permutation-invariant self-attention mechanism. Further investigation suggests that simple linear layers augmented with the Patch mechanism may outperform complex Transformer-based LTSF models. Moreover, diverging from models that use channel independence, our research underscores the importance of cross-variable interactions in enhancing the performance of multivariate time series forecasting. The interaction information between variables is highly valuable but has been misapplied in past studies, leading to suboptimal cross-variable models. Based on these insights, we propose a novel and simple Patch-based Decomposed MLP (PDMLP) for LTSF tasks. Specifically, we employ simple moving averages to extract smooth components and noise-containing residuals from time series data, engaging in semantic information interchange through channel mixing and specializing in random noise with channel independence processing. The PDMLP model consistently achieves state-of-the-art results on several real-world datasets. We hope this surprising finding will spur new research directions in the LTSF field and pave the way for more efficient and concise solutions.

Related papers

• 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)
• CMamba: Channel Correlation Enhanced State Space Models for Multivariate Time Series Forecasting [18.50360049235537]
  Mamba, a state space model, has emerged with robust sequence and feature mixing capabilities. Capturing cross-channel dependencies is critical in enhancing performance of time series prediction. We introduce a refined Mamba variant tailored for time series forecasting.
  arXiv  Detail & Related papers  (2024-06-08T01:32:44Z)
• UniTST: Effectively Modeling Inter-Series and Intra-Series Dependencies for Multivariate Time Series Forecasting [98.12558945781693]
  We propose a transformer-based model UniTST containing a unified attention mechanism on the flattened patch tokens. Although our proposed model employs a simple architecture, it offers compelling performance as shown in our experiments on several datasets for time series forecasting.
  arXiv  Detail & Related papers  (2024-06-07T14:39:28Z)
• Adaptive Multi-Scale Decomposition Framework for Time Series Forecasting [26.141054975797868]
  We propose a novel Adaptive Multi-Scale Decomposition (AMD) framework for time series forecasting (TSF) Our framework decomposes time series into distinct temporal patterns at multiple scales, leveraging the Multi-Scale Decomposable Mixing (MDM) block. Our approach effectively models both temporal and channel dependencies and utilizes autocorrelation to refine multi-scale data integration.
  arXiv  Detail & Related papers  (2024-06-06T05:27:33Z)
• TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series [57.4208255711412]
  Building on copula theory, we propose a simplified objective for the recently-introduced transformer-based attentional copulas (TACTiS) We show that the resulting model has significantly better training dynamics and achieves state-of-the-art performance across diverse real-world forecasting tasks.
  arXiv  Detail & Related papers  (2023-10-02T16:45:19Z)
• 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)
• CLMFormer: Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting System [46.39662315849883]
  Long-term time-series forecasting (LTSF) plays a crucial role in various practical applications. Existing Transformer-based models, such as Fedformer and Informer, often achieve their best performances on validation sets after just a few epochs. We propose a novel approach to address this issue by employing curriculum learning and introducing a memory-driven decoder.
  arXiv  Detail & Related papers  (2022-07-16T04:05:15Z)
• Bayesian Transformer Language Models for Speech Recognition [59.235405107295655]
  State-of-the-art neural language models (LMs) represented by Transformers are highly complex. This paper proposes a full Bayesian learning framework for Transformer LM estimation.
  arXiv  Detail & Related papers  (2021-02-09T10:55:27Z)
• Transformer Hawkes Process [79.16290557505211]
  We propose a Transformer Hawkes Process (THP) model, which leverages the self-attention mechanism to capture long-term dependencies. THP outperforms existing models in terms of both likelihood and event prediction accuracy by a notable margin. We provide a concrete example, where THP achieves improved prediction performance for learning multiple point processes when incorporating their relational information.
  arXiv  Detail & Related papers  (2020-02-21T13:48:13Z)

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.