本页提供题录、可核实摘要与阅读线索。AI 解读未完成全文核验，可能有误；具体假设、证明和结论请核对原文。仅依据书目生成的内容属于阅读导航，不是对论文结论的确认。
摘要保留来源文本，未经模型改写。材料不足时保留待解读状态；引用采用 OpenAlex 单条关联记录，不合计版本，缺失不视为零，也不代表论文质量。
正在核对对应版本的资源…
Recurrent Neural Networks (RNNs) have become competitive forecasting methods, as most notably shown in the winning method of the recent M4 competition. However, established statistical models such as exponential smoothing (ETS) and the autoregressive integrated moving average (ARIMA) gain their popularity not only from their high accuracy, but also because they are suitable for non-expert users in that they are robust, efficient, and automatic. In these areas, RNNs have still a long way to go. We present an extensive empirical study and an open-source software framework of existing RNN architectures for forecasting, and we develop guidelines and best practices for their use. For example, we conclude that RNNs are capable of modelling seasonality directly if the series in the dataset possess homogeneous seasonal patterns; otherwise, we recommend a deseasonalisation step. Comparisons against ETS and ARIMA demonstrate that (semi-) automatic RNN models are not silver bullets, but they are nevertheless competitive alternatives in many situations.