Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Haoyi Zhou, Shanghang Zhang, Jieqi Peng et al.
2021 · Proceedings of the AAAI Conference on Artificial Intelligence · 5,950 citations
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 inhere…
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