Wednesday 26 March 2025
Time series forecasting is a crucial task in many fields, such as finance and traffic management. However, it can be challenging to accurately predict future values when data arrives sequentially and is accompanied by temporal distribution shifts. A new approach has been proposed to tackle this problem by disentangling long-term dependencies from short-term changes.
The traditional methods for time series forecasting rely heavily on batch training paradigms, which are not suitable for online scenarios where data arrives one by one. In these situations, the models struggle to adapt to changing distributions and often produce suboptimal predictions. To address this issue, researchers have developed methods that control updates of latent states, but they fail to disentangle long-term dependencies from short-term changes.
The proposed approach uses a novel framework to disentangle long-term dependencies from short-term changes in time series data. This is achieved by modeling the data as a combination of two components: a long-term component that captures the underlying trends and patterns, and a short-term component that represents the abrupt changes. The authors argue that this decomposition enables the model to better adapt to changing distributions and improve forecasting accuracy.
The proposed framework consists of two main components: a long-term encoder that learns to represent the long-term dependencies in the data, and a short-term encoder that captures the short-term changes. The long-term encoder uses a recurrent neural network (RNN) architecture to learn the underlying trends and patterns in the data, while the short-term encoder employs a convolutional neural network (CNN) to identify abrupt changes.
To evaluate the effectiveness of this approach, the authors conducted experiments on several benchmark datasets. The results show that the proposed framework significantly outperforms existing methods for online time series forecasting. In particular, it achieves better performance in terms of mean square error and mean absolute error on datasets such as traffic and exchange rates.
The proposed approach has potential applications in various fields where accurate forecasting is crucial, such as finance, traffic management, and energy consumption prediction. The ability to disentangle long-term dependencies from short-term changes can lead to more reliable predictions and better decision-making in these domains.
In summary, the proposed framework offers a promising solution for online time series forecasting by disentangling long-term dependencies from short-term changes. Its effectiveness has been demonstrated through experiments on benchmark datasets, and it has potential applications in various fields where accurate forecasting is crucial.
Cite this article: “Disentangling Long-Term Dependencies from Short-Term Changes for Online Time Series Forecasting”, The Science Archive, 2025.
Time Series Forecasting, Online Learning, Long-Term Dependencies, Short-Term Changes, Recurrent Neural Networks, Convolutional Neural Networks, Traffic Management, Finance, Energy Consumption Prediction, Forecasting Accuracy.
