Screen Task Experiments for Eeg Signals Based on Ssvep Brain Computer Interface.

Fraga, SMFernandez; Escobedo, Centro n Mariano

doi:10.21474/ijar01/6612

Cited by 10 publications

(1 citation statement)

References 7 publications

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“…In the age of big data, sequence data is everywhere in life [1,2]. Time series prediction algorithms are becoming more and more important in many areas, such as financial market prediction [3], passenger demand forecasting [4], and heart signal prediction [5]. In most cases, time series data is multivariate.…”

Section: Introductionmentioning

confidence: 99%

DWNet: Dual‐Window Deep Neural Network for Time Series Prediction

et al. 2021

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Multivariate time series prediction is a very important task, which plays a huge role in climate, economy, and other fields. We usually use an Attention-based Encoder-Decoder network to deal with multivariate time series prediction because the attention mechanism makes it easier for the model to focus on the really important attributes. However, the Encoder-Decoder network has the problem that the longer the length of the sequence is, the worse the prediction accuracy is, which means that the Encoder-Decoder network cannot process long series and therefore cannot obtain detailed historical information. In this paper, we propose a dual-window deep neural network (DWNet) to predict time series. The dual-window mechanism allows the model to mine multigranularity dependencies of time series, such as local information obtained from a short sequence and global information obtained from a long sequence. Our model outperforms nine baseline methods in four different datasets.

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