mlsanityData to 7 Oct 2026

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

arXiv 2106.05860 ↗First posted 7 Jun 2021cs.LG stat.MLcited by 0

Not on the map. Why?

Abstract (arXiv)

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%.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 21 references that are in the map. First posted on arXiv 7 Jun 2021.

Builds on

11 of 21 references.

References · 11

  1. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,004
  2. 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 748
  3. 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecastingcited by 744
  4. 2015Learning Deconvolution Network for Semantic Segmentationcited by 365
  5. 2017A Multi-Horizon Quantile Recurrent Forecastercited by 211
  6. 2020Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmarkcited by 105
  7. 2017Dilated Recurrent Neural Networkscited by 83
  8. 2021Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSxcited by 63
  9. 2019Deep Learning for Time Series Forecasting: The Electric Load Casecited by 31
  10. 2019STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for Weather Forecastingcited by 23
  11. 2014Multi-Step-Ahead Time Series Prediction using Multiple-Output Support Vector Regressioncited by 17

Built on by

Not cited yet.