- The paper introduces Dish-TS, a general paradigm that mitigates both intra- and inter-space shifts in time series forecasting using a dual Conet framework.
- It employs a two-stage process with pre-forecast normalization and post-forecast denormalization coupled with a novel training strategy leveraging prior knowledge.
- Empirical results demonstrate that Dish-TS boosts forecasting accuracy by over 20% across multiple state-of-the-art models and diverse datasets.
Overview of Dish-TS: A Framework for Addressing Distribution Shifts in Time Series Forecasting
The paper "Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting" addresses a critical challenge in the domain of Time Series Forecasting (TSF) — the distribution shift. Distribution shift refers to the alterations in the data distribution over time, which poses significant obstacles to the predictive capabilities of TSF models. The authors categorize the distribution shift into two types: intra-space shift and inter-space shift. Intra-space shift refers to the changes in data distribution within the input space over time, while inter-space shift pertains to the distribution variances between input-space and output-space.
To combat these challenges, the authors introduce Dish-TS, a novel neural paradigm designed to address distribution shifts in TSF. This framework is model-agnostic, implying it can be paired with any deep learning TSF model to enhance its performance.
Methodological Insights
Dish-TS incorporates a two-stage process inspired by existing literature, which involves normalizing input data before forecasting and subsequently denormalizing the output after forecasting. A central component of Dish-TS is the introduction of the Coefficient Network (Conet), a flexible architecture that converts input sequences into learnable distribution coefficients. This allows for adaptive and robust modeling of data distribution.
Furthermore, the Dual-Conet framework within Dish-TS separately learns the distributions of input- and output-space, which helps to alleviate the identified intra- and inter-space shifts. This dual structure consists of BackConet for input-space distribution estimation and HoriConet for predicting output-space distribution.
A notable methodological advancement is the novel training strategy, utilizing prior knowledge to facilitate the intractable learning tasks inherent in Conet, specifically when predicting future distributions. This strategy improves the capacity of the HoriConet in capturing complex distributions by incorporating additional supervision signals.
Empirical Evaluation
The empirical analyses reveal that Dish-TS consistently enhances TSF performance across several datasets and models. The framework was tested with various state-of-the-art TSF models, including Informer, Autoformer, and N-BEATS. Results indicate a substantial improvement, with Dish-TS achieving average enhancements exceeding 20% in both univariate and multivariate forecasting tasks.
A particularly compelling aspect of the paper is the assertion that Dish-TS outperforms existing normalization techniques, such as RevIN, by addressing both intra-space and inter-space shifts. This is validated by empirical findings that demonstrate Dish-TS's superior ability to model non-stationary time series data, which is crucial in real-world applications requiring accurate forecasting in environments with dynamic data distributions.
Theoretical and Practical Implications
Theoretically, Dish-TS broadens the understanding and modeling capabilities of distribution shifts in TSF, offering a robust solution that enhances the generalization capabilities of forecasting models. By addressing both intra-space and inter-space distribution changes, this approach provides a deeper insight into the complex nature of time series data and how it can be manipulated to improve predictive accuracy.
From a practical standpoint, Dish-TS has the potential to significantly impact various applications of TSF, such as energy consumption prediction, traffic flow analysis, and meteorological forecasting. The paradigm opens avenues for further research and development in AI, especially in crafting models that remain robust in the face of data distribution changes.
In conclusion, Dish-TS represents an important methodological contribution to the field of time series forecasting, offering a versatile and scalable approach to mitigating distribution shifts. As future research expands on these findings, the continued development of frameworks like Dish-TS will be crucial in advancing the field towards even more accurate and reliable predictive models.