GAS-Norm: Score-Driven Adaptive Normalization for Non-Stationary Time Series Forecasting in Deep Learning

Autor: Urettini, Edoardo, Atzeni, Daniele, Ramjattan, Reshawn J., Carta, Antonio

Publikováno v: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM '24), October 21--25, 2024, Boise, ID, USA

Despite their popularity, deep neural networks (DNNs) applied to time series forecasting often fail to beat simpler statistical models. One of the main causes of this suboptimal performance is the data non-stationarity present in many processes. In p

Externí odkaz: http://arxiv.org/abs/2410.03935