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Attention-based novel neural network for mixed frequency data

It is a common fact that data (features, characteristics or variables) are collected at different sampling frequencies in some fields such as economic and industry. The existing methods usually either ignore the difference from the different sampling frequencies or hardly take notice of the inherent temporal characteristics in mixed frequency data. The authors propose an innovative dual attention ‐ based neural network for mixed frequency data (MID ‐ DualAtt), in order to utilize the inherent temporal characteristics and select the input characteristics reasonably without losing information. According to the authors’ knowledge, this is the first study to use the attention mechanism to process mixed frequency data. The MID ‐ DualAtt model uses the frequency alignment method to transform the high ‐‐ frequency variables into observation vectors at low frequency, and more critical input characteristics are selected for the current prediction index by attention mechanism. The temporal characteristics are explored by the encoder ‐ decoder with attention based on long ‐ short ‐ term memory networks (LSTM). The proposed MID ‐ DualAtt has been tested in practical application, and the experimental results show that it has better prediction ability than the compared models.