  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%.
