A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis (2310.11959v2)

Abstract: Time series data, including univariate and multivariate ones, are characterized by unique composition and complex multi-scale temporal variations. They often require special consideration of decomposition and multi-scale modeling to analyze. Existing deep learning methods on this best fit to univariate time series only, and have not sufficiently considered sub-series modeling and decomposition completeness. To address these challenges, we propose MSD-Mixer, a Multi-Scale Decomposition MLP-Mixer, which learns to explicitly decompose and represent the input time series in its different layers. To handle the multi-scale temporal patterns and multivariate dependencies, we propose a novel temporal patching approach to model the time series as multi-scale patches, and employ MLPs to capture intra- and inter-patch variations and channel-wise correlations. In addition, we propose a novel loss function to constrain both the mean and the autocorrelation of the decomposition residual for better decomposition completeness. Through extensive experiments on various real-world datasets for five common time series analysis tasks, we demonstrate that MSD-Mixer consistently and significantly outperforms other state-of-the-art algorithms with better efficiency.

An Overview of "A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis"

The paper under review introduces the MSD-Mixer, an innovative architecture leveraging multi-layer perceptrons (MLPs) for the analysis of time series data. This work aims to address prominent challenges in time series analysis, focusing on univariate and multivariate datasets characterized by intricate temporal patterns and composition issues that conventional deep learning approaches have not adequately explored.

Core Contributions and Methodology

The authors propose a novel MLP-based architecture, the MSD-Mixer, designed to overcome the limitations of existing models, which typically cater to univariate time series. The central innovation lies in the layered decomposition of input times series, allowing for explicit representation of temporal patterns across different scales. This approach attempts to disentangle complex periodic and trend-cyclic patterns efficiently, which are often superimposed with noise.

1. Multi-Scale Temporal Patching: By introducing a temporal patching strategy, the MSD-Mixer effectively segments input data into non-overlapping patches of varying sizes tailored to capture different scales within the time series. This design enables modeling of both local and global temporal dynamics through the generation of multi-scale patches, facilitating an enhanced representation of temporal dependencies.
2. Dimensional MLP Mixing: MSD-Mixer employs MLPs across different dimensions to harness intra- and inter-patch variations and address channel-wise correlations. This method ensures a comprehensive exploration of dependencies within multivariate datasets, previously addressed by more computationally intense architectures like Transformers.
3. Residual Loss Function: The paper introduces an advanced loss function that optimizes the model's decomposition process by constraining both the mean and autocorrelation of the residuals. This effort ensures a more complete decomposition of the temporal data into meaningful components, a critical aspect often overlooked, affecting models' efficacy, especially in multivariate contexts.

Experimental Validation

The MSD-Mixer was tested rigorously across a wide range of datasets encompassing diverse domains such as energy, transportation, and finance. The experimental framework incorporated five customary time series tasks: long-term and short-term forecasting, imputation, anomaly detection, and classification.

An impressive aspect of this work is the detailed comparison against state-of-the-art models from deep learning paradigms (CNNs, Transformers, and MLPs), including specific architectures like TimesNet, PatchTST, and ETSformer. Across various benchmarks—extending from real-world dataset evaluations to synthetic datasets—the MSD-Mixer achieved superior performance, consistently obtaining lower error metrics in tasks like forecasting (up to 9.8% improvement in MSE) and enhancing classification accuracy (up to 36.3% in Mean Rank).

Implications and Future Research

The MSD-Mixer embodies a significant step forward in time series analysis by showcasing how MLPs, when appropriately adapted with novel architectural enhancements, can challenge more complex models in efficiency and effectiveness. The decomposition-focused approach not only aids in better model interpretability but also aligns with the ongoing exploration of reducing model complexity while maintaining or even boosting performance metrics.

Future research can be directed towards exploring the integration of the MSD-Mixer with other temporal models and extending this framework to accommodate more massive datasets or streaming time series for real-time applications. Further adaptations could also aim to optimize model training efficiency and enhance the model’s ability to generalize across even more diversified and unseen time series scenarios.

In summary, this paper proposes a robust, flexible approach to tackle intricate challenges inherent in time series data, advocating for MLP structures as viable contenders in an arena often dominated by Transformer-based models. Through methodological innovation and comprehensive empirical validation, the MSD-Mixer emerges as a compelling contribution to the landscape of time series analysis.