AverageTime: Enhance Long-Term Time Series Forecasting with Simple Averaging
This work addresses long-term time series forecasting for applications requiring efficient and accurate predictions, though it appears incremental as it builds on existing methods like averaging and clustering.
The paper tackled the challenge of modeling dependencies in long-term time series forecasting by proposing AverageTime, a simple structure using averaging operations and mixed channel embedding, which outperformed state-of-the-art models in predictive performance on real-world datasets.
Long-term time series forecasting focuses on leveraging historical data to predict future trends. The core challenge lies in effectively modeling dependencies both within sequences and channels. Convolutional Neural Networks and Linear models often excel in sequence modeling but frequently fall short in capturing complex channel dependencies. In contrast, Transformer-based models, with their attention mechanisms applied to both sequences and channels, have demonstrated strong predictive performance. Our research proposes a new approach for capturing sequence and channel dependencies: AverageTime, an exceptionally simple yet effective structure. By employing mixed channel embedding and averaging operations, AverageTime separately captures correlations for sequences and channels through channel mapping and result averaging. In addition, we integrate clustering methods to further accelerate the model's training process. Experiments on real-world datasets demonstrate that AverageTime surpasses state-of-the-art models in predictive performance while maintaining efficiency comparable to lightweight linear models. This provides a new and effective framework for modeling long time series.