Multi-encoder and single-decoder sequence modeling method based on Informer

Gongwu Chen; Shuang Shan

doi:10.1117/12.3004955

9 October 2023 Multi-encoder and single-decoder sequence modeling method based on Informer

Gongwu Chen, Shuang Shan

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 1279120 (2023) https://doi.org/10.1117/12.3004955
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

In the original Informer model, an encoder and a decoder are used to process the entire time series. Although this structure is simple, its computational cost and memory requirements are very high for long sequences or high-dimensional data, making it difficult to perform effective training and prediction. In addition, since there may be large differences in the features of different time periods in the sequence, it may be difficult for a single encoder to adequately model them, resulting in a drop in predictive performance. Therefore, in this paper, by adopting multiple encoders to process the features of different time periods separately, the local structure of the sequence can be more fully utilized, thereby improving the accuracy and robustness of the model. In addition, multiple encoders can process different parts of the sequence in parallel, thus speeding up the training and prediction process and improving the efficiency of the model. In experiments, we used a set of time series datasets to evaluate our model. The results show that compared with the original Informer model, our multi-encoder single-decoder model can significantly improve the prediction accuracy and achieve higher speed under the same computing resources.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Gongwu Chen and Shuang Shan "Multi-encoder and single-decoder sequence modeling method based on Informer", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 1279120 (9 October 2023); https://doi.org/10.1117/12.3004955

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KEYWORDS

Data modeling

Education and training

Design and modelling

Modeling

Performance modeling

Transformers

Process modeling

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