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%.
@article{arxiv.2106.05860,
title = {DMIDAS: Deep Mixed Data Sampling Regression for Long Multi-Horizon Time Series Forecasting},
author = {Cristian Challu and Kin G. Olivares and Gus Welter and Artur Dubrawski},
journal= {arXiv preprint arXiv:2106.05860},
year = {2021}
}