DMIDAS: Deep Mixed Data Sampling Regression for Long Multi-Horizon Time Series Forecasting
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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 of NBEATS by nearly 70
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