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TSMixer: An All-MLP Architecture for Time Series Forecasting (2303.06053v5)

Published 10 Mar 2023 in cs.LG and cs.AI

Abstract: Real-world time-series datasets are often multivariate with complex dynamics. To capture this complexity, high capacity architectures like recurrent- or attention-based sequential deep learning models have become popular. However, recent work demonstrates that simple univariate linear models can outperform such deep learning models on several commonly used academic benchmarks. Extending them, in this paper, we investigate the capabilities of linear models for time-series forecasting and present Time-Series Mixer (TSMixer), a novel architecture designed by stacking multi-layer perceptrons (MLPs). TSMixer is based on mixing operations along both the time and feature dimensions to extract information efficiently. On popular academic benchmarks, the simple-to-implement TSMixer is comparable to specialized state-of-the-art models that leverage the inductive biases of specific benchmarks. On the challenging and large scale M5 benchmark, a real-world retail dataset, TSMixer demonstrates superior performance compared to the state-of-the-art alternatives. Our results underline the importance of efficiently utilizing cross-variate and auxiliary information for improving the performance of time series forecasting. We present various analyses to shed light into the capabilities of TSMixer. The design paradigms utilized in TSMixer are expected to open new horizons for deep learning-based time series forecasting. The implementation is available at https://github.com/google-research/google-research/tree/master/tsmixer

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Authors (5)
  1. Si-An Chen (10 papers)
  2. Chun-Liang Li (60 papers)
  3. Nate Yoder (2 papers)
  4. Tomas Pfister (89 papers)
  5. Sercan O. Arik (40 papers)
Citations (116)
View on Semantic Scholar

Summary

An Overview of TSMixer: An All-MLP Architecture for Time Series Forecasting

The research paper under consideration presents TSMixer, a novel approach to time series forecasting that leverages a straightforward architecture based on Multi-Layer Perceptrons (MLPs). Traditional models often rely on complex structures such as recurrent neural networks or attention mechanisms to handle the dynamics of multivariate time series data. However, this paper explores the comparative efficacy of simpler linear models and proposes TSMixer as an effective alternative.

Methodology and Architecture

TSMixer diverges from conventional deep learning models by employing MLPs in a way that exploits both temporal and cross-variate information. The architecture consists of layers that alternate between time-mixing and feature-mixing MLPs. This design efficiently captures temporal patterns and interactions between features while controlling the growth of model parameters.

The model operates with two key components:

  • Time-mixing MLPs: Shared across all features, they process data along the temporal dimension to leverage sequential dependencies.
  • Feature-mixing MLPs: Applied across time steps, these capture interdependencies among different variables.

Residual connections and normalization techniques are incorporated to enhance model training and maintain stable learning dynamics.

Empirical Evaluation

The proposed TSMixer architecture demonstrates compelling results across various datasets. On several commonly used academic benchmarks for long-term forecasting, TSMixer exhibits performance on par with state-of-the-art univariate models and surpasses some complex multivariate models. Notably, the model shines on the M5 competition dataset, a challenging retail dataset that benefits significantly from modeling cross-variate interactions.

The findings indicate that while conventional multivariate models suffer from overfitting when unnecessary cross-variate information is modeled, TSMixer balances the complexities by efficiently utilizing available data, demonstrating strong generalization capabilities.

Implications and Future Prospects

The implications of this research are far-reaching both practically and theoretically. TSMixer's simplicity and efficiency suggest that complex inductive biases embedded in deep learning architectures might be unnecessary for achieving strong performance in some real-world scenarios. This has potential ramifications for the deployment of more lightweight and interpretable models in industry settings, especially where resource constraints are a concern.

Theoretically, the paper encourages a reevaluation of the roles of model complexity and data dependencies in time series forecasting. The demonstrated ability of linear and MLP-based models to efficiently capture essential dynamics invites further exploration into model architectures that optimize simplicity and predictive power.

Future research paths might include extending TSMixer's applicability to even larger datasets and more complex auxiliary information structures, thus enhancing the robustness and versatility of AI in time series analytics.

In conclusion, TSMixer presents a convincing case for reevaluating the paradigms of deep learning in time series forecasting, emphasizing the potential for simpler, more efficient models that do not compromise on performance.

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