A CNN autoencoder that computes attention scores from variate correlations replaces self-attention, cutting resource use while improving multivariate time-series forecast accuracy.
A time series is worth 64 words: Long-term forecasting with transformers
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CASA: CNN Autoencoder-based Score Attention for Efficient Multivariate Long-term Time-series Forecasting
A CNN autoencoder that computes attention scores from variate correlations replaces self-attention, cutting resource use while improving multivariate time-series forecast accuracy.