论文标题
n命中：时间序列预测的神经层次插值
N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting
论文作者
论文摘要
神经预测的最新进展加速了大规模预测系统的性能。然而，长途预测仍然是一项非常艰巨的任务。困扰该任务的两个常见挑战是预测的波动及其计算复杂性。我们介绍了N-HITS，该模型通过结合新的分层插值和多率数据采样技术来解决挑战。这些技术使提出的方法能够顺序组装其预测，在分解输入信号并合成预测的同时，强调了不同频率和尺度的组件。我们证明，在平稳性的情况下，分层插值技术可以有效地近似任意长的视野。此外，我们从长远的预测文献中进行了广泛的大规模数据集实验，这证明了我们方法比最新方法的优势，其中N-HITS在最新的变压器架构中的平均准确度比最新的20％的平均准确性提高了，同时将计算时间降低（50次）。我们的代码可在bit.ly/3va5dot上找到
Recent progress in neural forecasting accelerated improvements in the performance of large-scale forecasting systems. Yet, long-horizon forecasting remains a very difficult task. Two common challenges afflicting the task are the volatility of the predictions and their computational complexity. We introduce N-HiTS, a model which addresses both challenges by incorporating novel hierarchical interpolation and multi-rate data sampling techniques. These techniques enable the proposed method to assemble its predictions sequentially, emphasizing components with different frequencies and scales while decomposing the input signal and synthesizing the forecast. We prove that the hierarchical interpolation technique can efficiently approximate arbitrarily long horizons in the presence of smoothness. Additionally, we conduct extensive large-scale dataset experiments from the long-horizon forecasting literature, demonstrating the advantages of our method over the state-of-the-art methods, where N-HiTS provides an average accuracy improvement of almost 20% over the latest Transformer architectures while reducing the computation time by an order of magnitude (50 times). Our code is available at bit.ly/3VA5DoT
