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Found 8 papers, 4 papers with code
FusionSF: Fuse Heterogeneous Modalities in a Vector Quantized Framework for Robust Solar Power Forecasting
Current research predominantly relies on historical solar power data or numerical weather prediction in a single-modality format, ignoring the complementary information provided in different modalities.
GCformer: An Efficient Framework for Accurate and Scalable Long-Term Multivariate Time Series Forecasting
On the other hand, the long input sequence usually leads to large model size and high time complexity.
SaDI: A Self-adaptive Decomposed Interpretable Framework for Electric Load Forecasting under Extreme Events
In this paper, we propose a novel forecasting framework, named Self-adaptive Decomposed Interpretable framework~(SaDI), which ensembles long-term trend, short-term trend, and period modelings to capture temporal characteristics in different components.
TreeDRNet:A Robust Deep Model for Long Term Time Series Forecasting
Various deep learning models, especially some latest Transformer-based approaches, have greatly improved the state-of-art performance for long-term time series forecasting. However, those transformer-based models suffer a severe deterioration performance with prolonged input length, which prohibits them from using extended historical info. Moreover, these methods tend to handle complex examples in long-term forecasting with increased model complexity, which often leads to a significant increase in computation and less robustness in performance(e. g., overfitting).
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting
Recent studies have shown that deep learning models such as RNNs and Transformers have brought significant performance gains for long-term forecasting of time series because they effectively utilize historical information.
Ranked #3 on
Time Series Forecasting
on ETTh2 (96) Univariate
Transformers in Time Series: A Survey
From the perspective of network structure, we summarize the adaptations and modifications that have been made to Transformers in order to accommodate the challenges in time series analysis.
FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting
Although Transformer-based methods have significantly improved state-of-the-art results for long-term series forecasting, they are not only computationally expensive but more importantly, are unable to capture the global view of time series (e. g. overall trend).
Hybrid Attention Networks for Flow and Pressure Forecasting in Water Distribution Systems
Specifically, a hybrid spatial attention mechanism that employs inputs along temporal and spatial axes is proposed.
