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Found 4 papers, 4 papers with code
Take an Irregular Route: Enhance the Decoder of Time-Series Forecasting Transformer
With the development of Internet of Things (IoT) systems, precise long-term forecasting method is requisite for decision makers to evaluate current statuses and formulate future policies.
GBT: Two-stage transformer framework for non-stationary time series forecasting
It decouples the prediction process of TSFT into two stages, including Auto-Regression stage and Self-Regression stage to tackle the problem of different statistical properties between input and prediction sequences. Prediction results of Auto-Regression stage serve as a Good Beginning, i. e., a better initialization for inputs of Self-Regression stage.
FDNet: Focal Decomposed Network for Efficient, Robust and Practical Time Series Forecasting
Moreover, we propose focal input sequence decomposition method which decomposes input sequence in a focal manner for efficient and robust forecasting when facing Long Sequence Time series Input (LSTI) problem.
Respecting Time Series Properties Makes Deep Time Series Forecasting Perfect
Thanks to the core idea of respecting time series properties, no matter in which forecasting format, RTNet shows obviously superior forecasting performances compared with dozens of other SOTA time series forecasting baselines in three real-world benchmark datasets.
