Revisiting the Seasonal Trend Decomposition for Enhanced Time Series Forecasting
arXiv:2602.18465v1 Announce Type: new Abstract: Time series forecasting presents significant challenges in real-world applications across various domains. Building upon the decomposition of the time series, we enhance the architecture of machine learning models for better multivariate time series forecasting. To achieve this, we focus on the trend and seasonal components individually and investigate solutions to predict them with less errors. Recognizing that reversible instance normalization is effective only for the trend component, we take a different approach with the seasonal component by directly applying backbone models without any normalization or scaling procedures. Through these strategies, we successfully reduce error values of the existing state-of-the-art models and finally introduce dual-MLP models as more computationally efficient solutions. Furthermore, our approach consistently yields positive results with around 10% MSE average reduction across four state-of-the-art
arXiv:2602.18465v1 Announce Type: new Abstract: Time series forecasting presents significant challenges in real-world applications across various domains. Building upon the decomposition of the time series, we enhance the architecture of machine learning models for better multivariate time series forecasting. To achieve this, we focus on the trend and seasonal components individually and investigate solutions to predict them with less errors. Recognizing that reversible instance normalization is effective only for the trend component, we take a different approach with the seasonal component by directly applying backbone models without any normalization or scaling procedures. Through these strategies, we successfully reduce error values of the existing state-of-the-art models and finally introduce dual-MLP models as more computationally efficient solutions. Furthermore, our approach consistently yields positive results with around 10% MSE average reduction across four state-of-the-art baselines on the benchmark datasets. We also evaluate our approach on a hydrological dataset extracted from the United States Geological Survey (USGS) river stations, where our models achieve significant improvements while maintaining linear time complexity, demonstrating real-world effectiveness. The source code is available at https://github.com/Sanjeev97/Time-Series-Decomposition
Executive Summary
The article 'Revisiting the Seasonal Trend Decomposition for Enhanced Time Series Forecasting' introduces a novel approach to improving multivariate time series forecasting by focusing on the decomposition of trend and seasonal components. The authors propose using reversible instance normalization for the trend component and applying backbone models directly to the seasonal component, bypassing normalization. This methodology achieves a significant reduction in error values, with an average 10% decrease in Mean Squared Error (MSE) across four state-of-the-art baselines. The study also demonstrates real-world effectiveness through evaluations on a hydrological dataset from the USGS river stations, maintaining linear time complexity. The authors introduce dual-MLP models as computationally efficient solutions, further enhancing the practical applicability of their approach.
Key Points
- ▸ Enhanced time series forecasting through decomposition of trend and seasonal components.
- ▸ Use of reversible instance normalization for trend components and direct application of backbone models for seasonal components.
- ▸ Achievement of a 10% average reduction in MSE across state-of-the-art baselines.
- ▸ Real-world effectiveness demonstrated on a hydrological dataset with linear time complexity.
- ▸ Introduction of dual-MLP models for improved computational efficiency.
Merits
Innovative Approach
The article presents a novel method for time series forecasting by separately addressing trend and seasonal components, which is a departure from traditional methods that treat the series as a whole.
Empirical Validation
The study provides empirical evidence of its effectiveness through rigorous testing on benchmark datasets and real-world hydrological data, ensuring the reliability of the proposed methodology.
Computational Efficiency
The introduction of dual-MLP models offers a computationally efficient solution, making the approach more accessible and practical for real-world applications.
Demerits
Limited Scope of Evaluation
The study primarily focuses on hydrological datasets, which may limit the generalizability of the findings to other domains such as finance, healthcare, or economics.
Complexity of Implementation
The proposed methodology involves complex decomposition and normalization techniques, which may require significant expertise to implement effectively in practical scenarios.
Dependence on Backbone Models
The effectiveness of the approach is contingent on the performance of the backbone models used for the seasonal component, which may introduce variability in results.
Expert Commentary
The article 'Revisiting the Seasonal Trend Decomposition for Enhanced Time Series Forecasting' presents a significant advancement in the field of time series analysis. By decomposing the time series into trend and seasonal components and applying tailored normalization techniques, the authors achieve a notable reduction in forecasting errors. The study's empirical validation on both benchmark datasets and real-world hydrological data underscores its practical relevance. However, the methodology's complexity and dependence on backbone models may pose challenges for widespread adoption. The introduction of dual-MLP models offers a promising solution for computational efficiency, making the approach more viable for real-world applications. Overall, the article contributes valuable insights to the ongoing discourse on enhancing time series forecasting accuracy and efficiency, with potential implications for various domains.
Recommendations
- ✓ Further research should explore the applicability of the proposed methodology to other domains beyond hydrology to assess its generalizability.
- ✓ Future studies could investigate the integration of more advanced backbone models to enhance the performance of the seasonal component prediction.
