For many fields in real life, time series forecasting is essential. Recent studies have shown that Transformer has certain advantages when dealing with such problems, especially long sequence input and problems. In order to improve the efficiency local stability of Transformer, these combine CNN different structures. However, previous network models based on cannot make full use CNN, they not b...

In this paper, we survey the most recent advances in supervised machine learning (ML) and high-dimensional models for time-series forecasting. We consider both linear nonlinear alternatives. Among methods, pay special attention to penalized regressions ensemble of models. The methods considered paper include shallow deep neural networks, their feedforward recurrent versions, tree-based such as ...