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High fluctuation and self‐similarity are typical characteristics of financial time series. Furthermore, affected by market environment, such as regular announcement of important economic data, time/date‐sensitive fluctuations commonly exist in financial time series. However, the existing learning models were usually lack the consideration of essential characteristics of financial data, where both the fusion learning of multiple temporal features and the necessary attention to time‐sensitive fluctuations were ignored. Inspired by this, to represent the temporal characteristics of self‐similarity and reveal intrinsic feature details, in this article, time series and its features are converted into visibility graphs using the technique of Gramian Angular Fields, based on which convolutional long short‐term memory (ConvLSTM) is applied to implement multifeature fusion learning. Moreover, to capture the time/date‐sensitive fluctuation existing in financial time series, a subspace decomposition composed of the fuzzy control mechanism is first introduced into the ConvLSTM model, which considerably improves the prediction performance. On the basis of the proposed learning model, a concise intelligent trading strategy is designed. By using real foreign exchange data, various experiments are implemented to show the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach is developed to deal with multiple‐attribute group decision‐making (MAGDM) problem under q‐rung orthopair fuzzy environment. Firstly, some operators have been proposed to aggregate q‐rung orthopair fuzzy information, such as the q‐rung orthopair fuzzy generalized power averaging (q‐ROFGPA) operator, the q‐rung orthopair fuzzy generalized power weighted averaging (q‐ROFGPWA) operator, the q‐rung orthopair fuzzy generalized power geometric (q‐ROFGPG) operator, and the q‐rung orthopair fuzzy generalized power weighted geometric (q‐ROFGPWG) operator. In addition, some desirable properties and special cases of these operators are discussed. Second, a novel approach is developed to solve MAGDM problem under the q‐rung orthopair fuzzy environment based on the proposed q‐ROFGPWA and q‐ROFGPWG operators. Finally, a practical example is given to illustrate the application of the proposed method, and further the sensitivity analysis and comparative analysis are carried out. [ABSTRACT FROM AUTHOR]

In the broadband era, narrowband short message service (SMS) is still the most popular wireless data service. Many studies have been conducted to investigate the performance of SMS based on the arrival rates of short messages. From Chunghwa Telecom's commercial SMS call data records, we observed that even if the SMS arrival rates are the same, the distributions for the number of SMS arrivals per half hour are quite different for various observed days. We further identify that for the SMS traffic in a specific day, there are non-burst and burst periods. This paper investigates the SMS behaviors on weekdays, weekends, and holidays (specifically, new years' days and eves). With the assistance of kernel-based fitting method, we derive the SMS arrival number distributions of various traffic types and observed days. Our approach fits each SMS arrival number distribution by three cubic polynomial functions that can accurately capture the SMS behaviors. On the basis of the SMS arrival number distributions derived from our model, the mobile operators have better understanding about the volumes of short messages in different times and days, which can be used to design more flexible short message charging rates. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]