Although artificial intelligence (AI) has made significant progress in understanding molecules in a wide range of fields, existing models generally acquire the single cognitive ability from the single molecular modality.
Most state-of-the-art methods focus on designing temporal convolution-based models, but the limitations on modeling long-term temporal dependencies and inflexibility of temporal convolutions limit the potential of these models.
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In this way, if a key segment has a high correlation score with the query segment, its successive segment contributes more to the prediction of the query segment.
Long-term time series forecasting is widely used in real-world applications such as financial investment, electricity management and production planning.