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

• URL: http://arxiv.org/abs/2106.05860v1
• Date: Mon, 7 Jun 2021 22:36:38 GMT
• Title: DMIDAS: Deep Mixed Data Sampling Regression for Long Multi-Horizon Time Series Forecasting
• Authors: Cristian Challu, Kin G. Olivares, Gus Welter, Artur Dubrawski
• Abstract summary: We develop a method to predict long-term energy prices using high-frequency healthcare and electricity price data. We improve the prediction accuracy by 5% over state-of-the-art models, reducing the number of parameters of NBEATS by nearly 70%.
• Score: 13.458489651961106
• License: http://creativecommons.org/licenses/by/4.0/
• 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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