In this paper, we introduce a novel model selection approach to time series forecasting. For linear stationary processes, such as AR processes, the direction of time is independent of the model ...

This study bridges classical time-series econometrics with modern machine learning by establishing theoretical performance guarantees for recurrent neural networks (RNNs) applied to complex ...

Improving the accuracy of long-term multivariate time series forecasting is important for practical applications. Various Transformer-based solutions emerging for time series forecasting. Recently, ...