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Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures

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arxiv 2207.01186 v1 pith:LV6BLGYG submitted 2022-07-04 cs.LG cs.AI

Tianping Zhang , Yizhuo Zhang , Wei Cao , Jiang Bian , Xiaohan Yi , Shun Zheng , Jian Li This is my paper
classification cs.LGcs.AI
keywords lighttsforecastingmethodsseriestimebenchmarkcomparedcomplex
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Multivariate time series forecasting has seen widely ranging applications in various domains, including finance, traffic, energy, and healthcare. To capture the sophisticated temporal patterns, plenty of research studies designed complex neural network architectures based on many variants of RNNs, GNNs, and Transformers. However, complex models are often computationally expensive and thus face a severe challenge in training and inference efficiency when applied to large-scale real-world datasets. In this paper, we introduce LightTS, a light deep learning architecture merely based on simple MLP-based structures. The key idea of LightTS is to apply an MLP-based structure on top of two delicate down-sampling strategies, including interval sampling and continuous sampling, inspired by a crucial fact that down-sampling time series often preserves the majority of its information. We conduct extensive experiments on eight widely used benchmark datasets. Compared with the existing state-of-the-art methods, LightTS demonstrates better performance on five of them and comparable performance on the rest. Moreover, LightTS is highly efficient. It uses less than 5% FLOPS compared with previous SOTA methods on the largest benchmark dataset. In addition, LightTS is robust and has a much smaller variance in forecasting accuracy than previous SOTA methods in long sequence forecasting tasks.

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Cited by 9 Pith papers

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

  1. Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting

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    CosDir aligns the first-difference vectors of predictions and targets via cosine similarity, improving directional accuracy while preserving MSE.

  2. AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE

    cs.LG 2026-06 unverdicted novelty 6.0 of 10

    AnchorMoE turns multivariate time-series classification into an additive Mixture-of-Experts over multi-view patches, with orthogonal anchors and reliability gates that suppress noise while matching strong classifiers.

  3. ARIES: Relation Assessment and Model Recommendation for Deep Time Series Forecasting

    cs.LG 2025-09 conditional novelty 6.0 of 10

    ARIES shows that deep forecasting models have consistent performance preferences tied to time series properties, and uses those preferences to recommend models for new datasets.

  4. Modular Foundation Models for Time-Series Perception in Digital Twins

    cs.LG 2026-07 conditional novelty 5.0 of 10

    A gated bank of frozen self-supervised time-series encoders, aligned and aggregated by a Transformer, supports competitive multi-task perception for digital twins and hydro-generator virtual sensing.

  5. MSTN: A Lightweight and Fast Model for General TimeSeries Analysis

    cs.LG 2025-11 unverdicted novelty 5.0 of 10

    MSTN is a lightweight hybrid model that reports new state-of-the-art results on 33 of 40 time series benchmarks for imputation, forecasting, and classification while using under one million parameters and sub-second i...

  6. CaReTS: A Multi-Task Framework Unifying Classification and Regression for Time Series Forecasting

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    CaReTS forecasts multi-step time series by combining a trend classifier with a deviation regressor in a residual, uncertainty-weighted multi-task framework.

  7. 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.

  8. Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives

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    TimesCLIP aligns image-based and text-based views of the same time series via contrastive learning to improve forecasting accuracy on several benchmarks, but the full multimodal model is not used on two of the six lon...

  9. TFKAN: Time-Frequency KAN for Long-Term Time Series Forecasting

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    TFKAN places Kolmogorov-Arnold Networks directly on FFT coefficients alongside a time-domain KAN branch, improving long-term forecast accuracy on seven benchmark datasets.

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