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arXiv · 2106.05860

DMIDAS: Deep Mixed Data Sampling Regression for Long Multi-Horizon Time Series Forecasting

Cristian Challu·Kin G. Olivares·Gus Welter·Artur Dubrawski
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Abstract

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%.

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Cristian Challu, Kin G. Olivares, Gus Welter, Artur Dubrawski. 2021-06-07. DMIDAS: Deep Mixed Data Sampling Regression for Long Multi-Horizon Time Series Forecasting. https://arxiv.org/abs/2106.05860

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