ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-Step-Ahead Time-Series Forecasting

Recurrent and convolutional neural networks are the most common architectures used for time series forecasting in deep learning literature. These networks use parameter sharing by repeating a set of fixed architectures with fixed parameters over time or space. The result is that the overall architec...

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Veröffentlicht in:	arXiv.org 2020-06
Hauptverfasser:	Joel Janek Dabrowski, Zhang, YiFan, Rahman, Ashfaqur
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Computer Science - Learning Deep learning Forecasting Invariants Machine learning Mathematical models Neural networks Parameters Statistics - Machine Learning
Online-Zugang:	Volltext
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