WPMixer: Efficient Multi-Resolution Mixing for Long-Term Time Series Forecasting

• URL: http://arxiv.org/abs/2412.17176v1
• Date: Sun, 22 Dec 2024 22:08:16 GMT
• Title: WPMixer: Efficient Multi-Resolution Mixing for Long-Term Time Series Forecasting
• Authors: Md Mahmuddun Nabi Murad, Mehmet Aktukmak, Yasin Yilmaz,
• Abstract summary: We propose Wavelet Patch Patch Mixer (WPMixer), a novel multi-resolution wavelet decomposition model for time series forecasting. Our model significantly outperforms state-of-the-art-based and transformer-based models for long-term time series forecasting.
• Score: 20.29110166475336
• License:
• Abstract: Time series forecasting is crucial for various applications, such as weather forecasting, power load forecasting, and financial analysis. In recent studies, MLP-mixer models for time series forecasting have been shown as a promising alternative to transformer-based models. However, the performance of these models is still yet to reach its potential. In this paper, we propose Wavelet Patch Mixer (WPMixer), a novel MLP-based model, for long-term time series forecasting, which leverages the benefits of patching, multi-resolution wavelet decomposition, and mixing. Our model is based on three key components: (i) multi-resolution wavelet decomposition, (ii) patching and embedding, and (iii) MLP mixing. Multi-resolution wavelet decomposition efficiently extracts information in both the frequency and time domains. Patching allows the model to capture an extended history with a look-back window and enhances capturing local information while MLP mixing incorporates global information. Our model significantly outperforms state-of-the-art MLP-based and transformer-based models for long-term time series forecasting in a computationally efficient way, demonstrating its efficacy and potential for practical applications.

Related papers

• xLSTM-Mixer: Multivariate Time Series Forecasting by Mixing via Scalar Memories [20.773694998061707]
  Time series data is prevalent across numerous fields, necessitating the development of robust and accurate forecasting models. We introduce xLSTM-Mixer, a model designed to effectively integrate temporal sequences, joint time-variable information, and multiple perspectives for robust forecasting. Our evaluations demonstrate xLSTM-Mixer's superior long-term forecasting performance compared to recent state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-10-22T11:59:36Z)
• 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)
• MGCP: A Multi-Grained Correlation based Prediction Network for Multivariate Time Series [54.91026286579748]
  We propose a Multi-Grained Correlations-based Prediction Network. It simultaneously considers correlations at three levels to enhance prediction performance. It employs adversarial training with an attention mechanism-based predictor and conditional discriminator to optimize prediction results at coarse-grained level.
  arXiv  Detail & Related papers  (2024-05-30T03:32:44Z)
• Unlocking the Power of Patch: Patch-Based MLP for Long-Term Time Series Forecasting [0.0]
  Recent studies have attempted to refine the Transformer architecture to demonstrate its effectiveness in Long-Term Time Series Forecasting tasks. We attribute the effectiveness of these models largely to the adopted Patch mechanism. We propose a novel and simple Patch-based components (PatchMLP) for LTSF tasks.
  arXiv  Detail & Related papers  (2024-05-22T12:12:20Z)
• PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations [49.80959046861793]
  We present PDETime, a novel LMTF model inspired by the principles of Neural PDE solvers. Our experimentation across seven diversetemporal real-world LMTF datasets reveals that PDETime adapts effectively to the intrinsic nature of the data.
  arXiv  Detail & Related papers  (2024-02-25T17:39:44Z)
• A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis [14.40202378972828]
  We propose MSD-Mixer, a Multi-Scale Decomposition-Mixer, which learns to explicitly decompose and represent the input time series in its different layers. We demonstrate that MSD-Mixer consistently and significantly outperforms other state-of-the-art algorithms with better efficiency.
  arXiv  Detail & Related papers  (2023-10-18T13:39:07Z)
• Time-LLM: Time Series Forecasting by Reprogramming Large Language Models [110.20279343734548]
  Time series forecasting holds significant importance in many real-world dynamic systems. We present Time-LLM, a reprogramming framework to repurpose large language models for time series forecasting. Time-LLM is a powerful time series learner that outperforms state-of-the-art, specialized forecasting models.
  arXiv  Detail & Related papers  (2023-10-03T01:31:25Z)
• TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting [13.410217680999459]
  Transformers have gained popularity in time series forecasting for their ability to capture long-sequence interactions. High memory and computing requirements pose a critical bottleneck for long-term forecasting. We propose TSMixer, a lightweight neural architecture composed of multi-layer perceptron (MLP) modules.
  arXiv  Detail & Related papers  (2023-06-14T06:26:23Z)
• Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement [51.55157852647306]
  Time series forecasting has been a widely explored task of great importance in many applications. It is common that real-world time series data are recorded in a short time period, which results in a big gap between the deep model and the limited and noisy time series. We propose to address the time series forecasting problem with generative modeling and propose a bidirectional variational auto-encoder equipped with diffusion, denoise, and disentanglement.
  arXiv  Detail & Related papers  (2023-01-08T12:20:46Z)
• Multi-scale Attention Flow for Probabilistic Time Series Forecasting [68.20798558048678]
  We propose a novel non-autoregressive deep learning model, called Multi-scale Attention Normalizing Flow(MANF) Our model avoids the influence of cumulative error and does not increase the time complexity. Our model achieves state-of-the-art performance on many popular multivariate datasets.
  arXiv  Detail & Related papers  (2022-05-16T07:53:42Z)
• TACTiS: Transformer-Attentional Copulas for Time Series [76.71406465526454]
  estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. We propose a versatile method that estimates joint distributions using an attention-based decoder. We show that our model produces state-of-the-art predictions on several real-world datasets.
  arXiv  Detail & Related papers  (2022-02-07T21:37:29Z)

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.