Pith. sign in

REVIEW 19 cited by

TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2405.14616 v1 pith:EOTMYFRJ submitted 2024-05-23 cs.LG cs.AI

Shiyu Wang , Haixu Wu , Xiaoming Shi , Tengge Hu , Huakun Luo , Lintao Ma , James Y. Zhang , Jun Zhou This is my paper · ORCID
classification cs.LGcs.AI
keywords forecastingseriesmultiscaletimetimemixervariationsdecompositiondisentangled
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Time series forecasting is widely used in extensive applications, such as traffic planning and weather forecasting. However, real-world time series usually present intricate temporal variations, making forecasting extremely challenging. Going beyond the mainstream paradigms of plain decomposition and multiperiodicity analysis, we analyze temporal variations in a novel view of multiscale-mixing, which is based on an intuitive but important observation that time series present distinct patterns in different sampling scales. The microscopic and the macroscopic information are reflected in fine and coarse scales respectively, and thereby complex variations can be inherently disentangled. Based on this observation, we propose TimeMixer as a fully MLP-based architecture with Past-Decomposable-Mixing (PDM) and Future-Multipredictor-Mixing (FMM) blocks to take full advantage of disentangled multiscale series in both past extraction and future prediction phases. Concretely, PDM applies the decomposition to multiscale series and further mixes the decomposed seasonal and trend components in fine-to-coarse and coarse-to-fine directions separately, which successively aggregates the microscopic seasonal and macroscopic trend information. FMM further ensembles multiple predictors to utilize complementary forecasting capabilities in multiscale observations. Consequently, TimeMixer is able to achieve consistent state-of-the-art performances in both long-term and short-term forecasting tasks with favorable run-time efficiency.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 19 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting

    cs.AI 2026-01 conditional novelty 6.0 of 10

    DropoutTS uses spectral reconstruction residuals to set per-sample dropout rates, reporting consistent robustness gains on six forecasting backbones without architectural changes.

  2. Dynamic Relational Priming Improves Transformer in Multivariate Time Series

    cs.LG 2025-09 conditional novelty 6.0 of 10

    Prime attention modulates attention keys and values per channel-pair and reports improved MTS forecasting accuracy across several benchmarks.

  3. WindFM: An Open-Source Foundation Model for Zero-Shot Wind Power Forecasting

    cs.LG 2025-09 conditional novelty 6.0 of 10

    A wind-specific foundation model, WindFM, uses hierarchical tokenization and autoregressive pre-training on the NREL WIND Toolkit to achieve state-of-the-art zero-shot wind power forecasts.

  4. Enhancing Fatigue Detection through Heterogeneous Multi-Source Data Integration and Cross-Domain Modality Imputation

    cs.RO 2025-07 conditional novelty 6.0 of 10

    A framework that imputes missing high-fidelity sensor channels (EEG, ECG) from shared wearable signals improves fatigue detection accuracy in a sensor-constrained target dataset in several configurations.

  5. Pre-training Time Series Models with Stock Data Customization

    cs.CE 2025-06 conditional novelty 6.0 of 10

    Stock Specialized Pre-trained Transformer (SSPT) uses stock code classification, sector classification, and moving average prediction as pre-training tasks, and reports improved stock selection returns and Sharpe rati...

  6. Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs

    cs.LG 2025-06 unverdicted novelty 6.0 of 10

    Time-R1 trains LLMs via supervised fine-tuning followed by reinforcement learning with a time-series-specific reward and non-uniform GRIP sampling to enable multi-step reasoning that improves forecasting accuracy.

  7. A Dynamic Stiefel Graph Neural Network for Efficient Spatio-Temporal Time Series Forecasting

    cs.LG 2025-06 conditional novelty 6.0 of 10

    DST-SGNN uses a Stiefel-manifold-constrained graph Fourier transform and a dynamic graph optimizer to achieve efficient and accurate spatio-temporal forecasting.

  8. Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series

    cs.LG 2025-05 conditional novelty 6.0 of 10

    A cluster-aware causal MLP-mixer with sequential evidence-accumulation scoring achieves state-of-the-art best-F1 on six multivariate time-series anomaly detection benchmarks.

  9. TimePro: Efficient Multivariate Long-term Time Series Forecasting with Variable- and Time-Aware Hyper-state

    cs.LG 2025-05 conditional novelty 6.0 of 10

    TimePro forecasts long multivariate series by scanning across variables with a Mamba-like model, then tuning each variable's hidden state at adaptively chosen time points.

  10. PIER: Physics-Informed Environmental Retrieval for Time-Series Modeling

    cs.LG 2026-07 conditional novelty 5.0 of 10

    PIER augments embedding-based retrieval for lake modeling with a physics-aware stream scored by local verifiers, improving water temperature and dissolved oxygen prediction across 356 lakes.

  11. Multi-Scale Convolution with Optimal Transport Attention Effect on Multivariate Time Series

    cs.LG 2026-07 conditional novelty 5.0 of 10

    MSC-OT fuses multi-scale convolution and Sinkhorn OT regularization into inverted attention, producing small but consistent MSE/MAE gains over iTransformer and several recent MTS baselines on five public datasets.

  12. From Noise to Precision: A Diffusion-Driven Approach to Zero-Inflated Precipitation Prediction

    cs.LG 2025-09 reject novelty 5.0 of 10

    A Gaussian-smoothing, Transformer, and DDPM denoising stack is claimed to cut MSE by up to 56.7% on zero-inflated precipitation forecasting, with a theoretical guarantee that does not hold.

  13. NeuTSFlow: Modeling Continuous Functions Behind Time Series Forecasting

    cs.LG 2025-07 reject novelty 5.0 of 10

    A flow-matching model with a neural-operator velocity field is proposed to forecast time series by transporting distributions over continuous functions, reporting top average rank on eight benchmarks.

  14. Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting

    cs.LG 2025-07 conditional novelty 5.0 of 10

    Using Fourier basis expansion to build time-frequency features improves long-term and short-term time series forecasting across linear, MLP, and transformer backbones.

  15. Bridging the Last Mile of Prediction: Enhancing Time Series Forecasting with Conditional Guided Flow Matching

    cs.LG 2025-07 conditional novelty 5.0 of 10

    CGFM uses an auxiliary model's predictions as the source for flow matching to learn forecast residuals and improve time series forecasts.

  16. DisMS-TS: Eliminating Redundant Multi-Scale Features for Time Series Classification

    cs.AI 2025-07 conditional novelty 5.0 of 10

    A multi-scale time series classifier that disentangles scale-shared and scale-specific features and reports improved accuracy on six benchmarks.

  17. PIPE: Physics-Informed Position Encoding for Alignment of Satellite Images and Time Series

    cs.LG 2025-05 conditional novelty 5.0 of 10

    A physics-informed positional encoding that embeds timestamps and latitude/longitude into a vision language model improves typhoon intensity and track forecasting on the Digital Typhoon dataset.

  18. QuantFlow: A Federated Mamba-Based Post-Transformer Foundation Model for Time-Series Forecasting

    cs.LG 2026-07 reject novelty 4.5 of 10

    QuantFlow combines inverted embeddings, bidirectional Mamba decoders, quantile regression, TSMixup, and three-round FedAvg to report competitive one-step MSE on ETTm1/Weather and retain accuracy across 20 non-IID clients.

  19. Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality

    cs.CV 2025-07 conditional novelty 4.0 of 10

    A new image-based similarity metric (TGSI) and a three-part training loss (SATL) that together aim to improve the geometric fidelity of time series forecasts.

Pith tools