TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting (2405.14616v1)

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.

An Expert Review of "TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting"

The paper "TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting" by Shiyu Wang et al. presents a novel approach to time series forecasting through a multi-scale data analysis framework. The methodology introduced diverges from traditional decomposition methods and periodicity analysis by leveraging a decomposable multiscale mixing architecture. This approach aims to effectively disentangle complex temporal variations often presented in real-world time series data.

Summary of Contributions

TimeMixer introduces a fully MLP-based architecture that effectively utilizes multiscale time series representations. The authors emphasize the importance of capturing distinct temporal patterns at various sampling scales. The model is divided into two primary blocks:

1. Past-Decomposable-Mixing (PDM): This block utilizes multiscale decomposed series, categorizing seasonal and trend components separately. By implementing separate mixing techniques—bottom-up for seasonal parts and top-down for trend parts—the architecture teases out fine and coarse scale patterns, respectively. This enables a more coherent aggregation of micro-scale seasonal information and macro-scale trend information.
2. Future-Multipredictor-Mixing (FMM): By ensembling multiple predictors, FMM exploits the complementary forecasting abilities of multiscale observations, leading to more accurate predictions.

TimeMixer achieves state-of-the-art performance across multiple datasets for both short and long-term forecasting tasks. This is corroborated by extensive experimentation over 18 benchmarks and comparison against 15 competitive models. Importantly, the architecture maintains computational efficiency, reinforcing the practicality of its application.

Analysis of Results

The results underscore that TimeMixer significantly outperforms existing models, particularly in handling intricate temporal variations with considerable improvements in datasets with low forecastability. The paper details comparison tables that depict improvements in MSE and MAE metrics across various datasets such as ETTm1, Solar-Energy, Electricity, and Weather where TimeMixer demonstrates a substantial reduction in forecast error.

Furthermore, the research presents visualizations to substantiate the effectiveness of multiscale mixing and decomposition components. These provides fine detail on the segregated forecasting capabilities across different scales—clearly illustrating how predictions from different scales can complement one another to enhance overall model predictions.

Implications and Future Directions

The implications in practical terms are expansive, given that accurate time series forecasting is crucial in domains like economics, energy, and traffic management. The utilization of both seasonal and trending patterns in TimeMixer can offer refined forecasting models in these fields.

Theoretically, this work unlocks new pathways in machine learning concerning time-series analysis. By showing effective multiscale mixing, the research points to potential enhancements in interpreting temporal data which can be expanded upon by subsequent studies.

Future developments may consider integrations with neural architectures that can further enhance the scalability and versatility of TimeMixer. Effective tuning of decomposition strategies and exploration of alternate multiscale representations might improve model performance even further, especially in dynamic and non-stationary environments.

In summary, TimeMixer stands as a substantial contribution to the field of time series forecasting. By merging decomposable and predictive multiscale capabilities, it provides a transformative approach that may serve as a foundation for future innovations in this sector. The robustness and efficiency of this model highlight promising strides in real-time predictive analytics.