Neural forecasting has shown significant improvements in the accuracy of large-scale systems, yet predicting extremely long horizons remains a challenging task. Two common problems are the volatility of the predictions and their computational complexity; we addressed them by incorporating smoothness regularization and mixed data sampling techniques to a well-performing multi-layer perceptron based architecture (NBEATS). We validate our proposed method, DMIDAS, on high-frequency healthcare and electricity price data with long forecasting horizons (~1000 timestamps) where we improve the prediction accuracy by 5% over state-of-the-art models, reducing the number of parameters of NBEATS by nearly 70%.

中文翻译：

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DMIDAS：用于长期多视域时间序列预测的深度混合数据采样回归

神经预测已显示出大规模系统准确性的显着提高，但预测极长的范围仍然是一项具有挑战性的任务。两个常见问题是预测的波动性及其计算复杂性；我们通过将平滑正则化和混合数据采样技术结合到性能良好的基于​​多层感知器的架构 (NBEATS) 来解决这些问题。我们在具有较长预测范围（约 1000 个时间戳）的高频医疗保健和电价数据上验证了我们提出的方法 DMIDAS，其中我们将预测准确度比最先进的模型提高了 5%，从而减少了参数数量NBEATS 增加了近 70%。