  Deep neural networks (DNNs) are one of the most highlighted methods in
machine learning. However, as DNNs are black-box models, they lack explanatory
power for their predictions. Recently, neural additive models (NAMs) have been
proposed to provide this power while maintaining high prediction performance.
In this paper, we propose a novel NAM approach for multivariate nowcasting (NC)
problems, which comprise an important focus area of machine learning. For the
multivariate time-series data used in NC problems, explanations should be
considered for every input value to the variables at distinguishable time
steps. By employing generalized additive models, the proposed NAM-NC
successfully explains each input value's importance for multiple variables and
time steps. Experimental results involving a toy example and two real-world
datasets show that the NAM-NC predicts multivariate time-series data as
accurately as state-of-the-art neural networks, while also providing the
explanatory importance of each input value. We also examine parameter-sharing
networks using NAM-NC to decrease their complexity, and NAM-MC's hard-tied
feature net extracted explanations with good performance.
