Informer Overview The Informer model was proposed in Informer: Beyond Efficient Transformer for Long Sequence Time Series Forecasting by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. This method introduces a Probabilistic Attention mechanism to select the "active" queries rather than the "lazy" queries and provides a sparse Transformer thus mitigating the quadratic compute and memory requirements of vanilla attention. The abstract from the paper is the following: Many real world applications require the prediction of long sequence time series, such as electricity consumption planning. Long sequence time series forecasting (LSTF) demands a high prediction capacity of the model, which is the ability to capture precise long range dependency coupling between output and input efficiently. Recent studies have shown the potential of Transformer to increase the prediction capacity. However, there are several severe issues with Transformer that prevent it from being directly applicable to LSTF, including quadratic time complexity, high memory usage, and inherent limitation of the encoder decoder architecture. To address these issues, we desig…
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