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

arXiv: https://arxiv.org/abs/2412.17176 · HTML: https://mlsanity.com/paper/2412.17176/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 22 Dec 2024.

## Builds on (most cited first, 6 of 6)

- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting](https://mlsanity.com/paper/2201.12740/) (2022)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction](https://mlsanity.com/paper/2106.09305/) (2021)

## Built on by (most cited first, 26 of 26)

- [Revisiting Multivariate Time Series Forecasting with Missing Values](https://mlsanity.com/paper/2509.23494/) (2025)
- [From Observations to States: Latent Time Series Forecasting](https://mlsanity.com/paper/2602.00297/) (2026)
- [Multi-Order Wavelet Derivative Transform for Deep Time Series Forecasting](https://mlsanity.com/paper/2505.11781/) (2025)
- [Functional MRI Time Series Generation via Wavelet-Based Image Transform and Spectral Flow Matching for Brain Disorder Identification](https://mlsanity.com/paper/2605.30387/) (2026)
- [FlowMixer: A Depth-Agnostic Neural Architecture for Interpretable Spatiotemporal Forecasting](https://mlsanity.com/paper/2505.16786/) (2025)
- [Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2506.00188/) (2025)
- [LLM4Fluid: Large Language Models as Generalizable Neural Solvers for Fluid Dynamics](https://mlsanity.com/paper/2601.21681/) (2026)
- [Existence Precedes Value: Joint Modeling of Observational Existence and Evolving States in Time Series Forecasting](https://mlsanity.com/paper/2606.13571/) (2026)
- [SWIFT: Mapping Sub-series with Wavelet Decomposition Improves Time Series Forecasting](https://mlsanity.com/paper/2501.16178/) (2025)
- [BEAT: Balanced Frequency Adaptive Tuning for Long-Term Time-Series Forecasting](https://mlsanity.com/paper/2501.19065/) (2025)
- [OLMA: One Loss for More Accurate Time Series Forecasting](https://mlsanity.com/paper/2505.11567/) (2025)
- [Invariant Representation Learning for Source-Free Time Series Forecasting with LLM-Centric Proxy Denoising](https://mlsanity.com/paper/2510.05589/) (2025)
- [HTMformer: Hybrid Time and Multivariate Transformer for Time Series Forecasting](https://mlsanity.com/paper/2510.07084/) (2025)
- [EMTSF:Extraordinary Mixture of SOTA Models for Time Series Forecasting](https://mlsanity.com/paper/2510.23396/) (2025)
- [DPWMixer: Dual-Path Wavelet Mixer for Long-Term Time Series Forecasting](https://mlsanity.com/paper/2512.02070/) (2025)
- [DecoKAN: Interpretable Decomposition for Forecasting Cryptocurrency Market Dynamics](https://mlsanity.com/paper/2512.20028/) (2025)
- [HPMixer: Hierarchical Patching for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2602.16468/) (2026)
- [Deep Time-Series Forecasting in 10 Years: A Survey](https://mlsanity.com/paper/2603.19899/) (2026)
- [Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting](https://mlsanity.com/paper/2605.06310/) (2026)
- [Generalizing Multi-Scale Time-Series Modeling with a Single Operator](https://mlsanity.com/paper/2605.31129/) (2026)
- [Multiple cyclicity and Wavelet Decomposition with Channel Correlation for Long-term Time Series Forecasting](https://mlsanity.com/paper/2606.17996/) (2026)
- [RAVEN: A Regime-Aware Variable-context Expert Network for Financial Time Series Forecasting](https://mlsanity.com/paper/2606.24062/) (2026)
- [Adaptive Multi-Scale Forecasting and Gate-Localized Conformal Prediction for Multivariate Nonstationary Time Series](https://mlsanity.com/paper/2607.23165/) (2026)
- [Mix&Fix-Net: A Dual-Stage Trajectory Prediction Model for AIS and Vision-Derived Vessel Data](https://mlsanity.com/paper/2608.19580/) (2026)
- [Towards Robust Time Series Learning via Capacity-Centric Modulation](https://mlsanity.com/paper/2609.39489/) (2026)
- [Most-Recent Anchoring with Recurrent Ordering for Time Series Forecasting](https://mlsanity.com/paper/2610.03494/) (2026)

## Abstract (arXiv)

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.