论文标题
无需注意时间序列的长期短期记忆预测
An Attention Free Long Short-Term Memory for Time Series Forecasting
论文作者
论文摘要
深度学习在时间序列分析中起着越来越重要的作用。我们专注于使用无注意机制的时间序列预测，一个更有效的框架，并为时间序列预测提出了一个新的体系结构，该预测似乎无法捕获时间依赖性。我们提出了一个使用无注意LSTM层建立的架构，该层面是克服有条件差异预测的线性模型。我们的发现证实了我们的模型的有效性，该模型还允许提高LSTM的预测能力，同时提高学习任务的效率。
Deep learning is playing an increasingly important role in time series analysis. We focused on time series forecasting using attention free mechanism, a more efficient framework, and proposed a new architecture for time series prediction for which linear models seem to be unable to capture the time dependence. We proposed an architecture built using attention free LSTM layers that overcome linear models for conditional variance prediction. Our findings confirm the validity of our model, which also allowed to improve the prediction capacity of a LSTM, while improving the efficiency of the learning task.
