Stock price prediction has always been an important application in time series predictions. Recently, deep neural networks have been employed extensively for financial time series tasks. The network typically requires a large amount of training samples to achieve high accuracy. However, in the stock market, the number of data points collected on a daily basis is limited in one year, which leads to insufficient training samples and accordingly results in an overfitting problem. Moreover, predicting stock price movement is affected by various factors in the stock market. Therefore, choosing appropriate input features for prediction models should be taken into account. To address these problems, this paper proposes a novel framework, named deep transfer with related stock information (DTRSI), which takes advantage of a deep neural network and transfer learning. First, a base model using long short-term memory (LSTM) cells is pre-trained based on a large amount of data, which are obtained from a number of different stocks, to optimize initial training parameters. Second, the base model is fine-tuned by using a small amount data from a target stock and different types of input features (constructed based on the relationship between stocks) in order to enhance performance. Experiments are conducted with data from top-five companies in the Korean market and the United States (US) market from 2012 to 2018 in terms of the highest market capitalization. Experimental results demonstrate the effectiveness of transfer learning and using stock relationship information in helping to improve model performance, and the proposed approach shows remarkable performance (compared to other baselines) in terms of prediction accuracy.
Mites of the genus Tyrophagus (Acari: Acaridae) are among the most widely distributed mites. The species in this genus cause damage to stored products and crops, and pose a threat to human health. However, the influence of Tyrophagus spp. in apiculture remains unknown. In 2022, a study focusing on the identification of Tyrophagus species within five apiaries was conducted in Chungcheongnam Province, Republic of Korea. Its specific objective was to investigate the presence of Tyrophagus mites in response to the reported high mortality of honey bee colonies in this area. Morphological identification and phylogenetic analysis using the mitochondrial gene cytochrome-c oxidase subunit 1 (CO1) confirmed for the first time the presence of the mite species Tyrophagus curvipenis in a honey bee colony in the Republic of Korea. Two honey bee pathogens were detected in the mite, a viral pathogen (deformed wing virus, DWV) and a protozoal pathogen (Trypanosoma spp.). The presence of the two honey bee pathogens in the mite suggests that this mite could contribute to the spread of related honey bee diseases. However, the direct influence of the mite T. curvipenis on honey bee health remains unknown and should be further investigated.
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