Nixtla: Deep Learning for Forecasting...
summary
Nixtla presents neuralforecast, a PyTorch library with datasets, evaluation tools and ready models (ESRNN and N-BEATS variants). It reproduces the M4-winning ESRNN, extends N-BEATS to support exogenous variables, and applies these models to electricity price and transformer datasets, reporting 20–25% lower error versus classical and recent baselines while being about 50× faster in training and inference. The project includes preconfigured hyperparameter search, runnable Colab/GPU demos, and code on GitHub.
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chapters (7)
- 0:00Introduction
- 7:13NBEATS Motivation and Contributions
- 9:39NBEATS Architecture
- 13:09N-HITS: Motivation
- 16:46N-HITS: Hierarchical Interpolation
- 18:57N-HITS: Empirical Results
- 20:35Nixtla: Forecasting Ecosystem
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