ArticleMethodsX2025
On the recurrent neural network model with robust expectile-based loss function in economic data forecasting.
Article in MethodsX, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Recurrent Neural Networks (RNNs), particularly their Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) variants, are standard methods for modeling sequential data. However, their robustness is often limited when faced with non-stationary and heterogeneous time series data. This limitation is largely due to their reliance on symmetric loss functions such as mean squared error, which implicitly assume homogeneous data patterns. To address this, we propose a new framework, Expectile-based Recurrent Neural Network (E-RNN), which integrates expectile regression into the RNN architecture. We implement and compare two E-RNN variants, namely E-LSTM and E-GRU, to obtain the best forecast. . By leveraging the asymmetric least squares loss function, the E-RNN model is able to model various parts of the conditional data distribution, not just its central tendency. This allows forecasting across scenarios, ranging from pessimistic to optimistic, by adjusting the asymmetric parameter (
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