 In our research, we separate the assessments of future economic conditions into  near and distant future economic conditions using learning from positive and unlabeled data (PU learning) , which is weakly supervised learning. The dataset is composed of several periods, and we develop a PU learning algorithm for efficient training, using the dataset with the time series.
<coherence>  In our research, we separate the assessments of future economic conditions into  near and distant future economic conditions using learning from positive and unlabeled data (PU learning) , which is weakly supervised learning. The dataset is composed of several periods, and we develop a PU learning algorithm for efficient training, using the dataset with the time series.
 In our research, we separate the assessments of future economic conditions into near and distant future economic conditions using learning from positive and unlabeled data (PU learning) . Because the dataset includes data from several periods, and we develop a PU learning algorithm for efficient training, using the dataset with the time series.