Average Rule Research Articles

Opinion dynamics with the increasing peer pressure and prejudice on the signed graph

In this paper, we propose the opinion dynamics model with the increasing peer pressure and the stubborn agents. Cooperation and competition between individuals are considered simultaneously in a social network. Similar to the signed DeGroot model, we adopt a weighted average update rule in our model. We derive conditions under which opinions converge to a fixed opinion distribution. In particular, we find conditions under which opinions reach consensus or polarization (bipartite consensus). Two examples are provided to illustrate the effectiveness of the obtained results.

Rational averaged gauss quadrature rules

It is important to be able to estimate the quadrature error in Gauss rules. Several approaches have been developed, including the evaluation of associated Gauss-Kronrod rules (if they exist), or the associated averaged Gauss and generalized averaged Gauss rules. Integrals with certain integrands can be approximated more accurately by rational Gauss rules than by Gauss rules. This paper introduces associated rational averaged Gauss rules and rational generalized averaged Gauss rules, which can be used to estimate the error in rational Gauss rules. Also rational Gauss-Kronrod rules are discussed. Computed examples illustrate the accuracy of the error estimates determined by these quadrature rules.

The use of moving average trading rules in Indonesian Stock Market

The development of behavioural finance stimulates the usefulness of simple trading rule. Decision biases are reflected in price movement, which is utilised by technical analysis indicators to generate trading rules. The main objective of this research is to evaluate the performance of the moving average (MA) trading rules, as a popular indicator in stock markets. The sensitivity of MA length is also examined to generate optimal return. We employ daily data of Indonesia composite index from the beginning of 1998 to the end of 2016 to examine the profitability of MA trading rule. The findings show that investors can apply MA trading rules in Indonesia stock market. Our supplementary analysis indicates that shorter (longer) MA produce higher (lower) return. The return of MA trading rules is also affected by trend. This paper contributes to trading strategy studies, particularly for stock exchanges in emerging markets. Our results recommend that selection of MA length in trading rules is important to create optimal return.

Distributed Multi-Sensor Fusion of PHD Filters With Different Sensor Fields of View

The paper addresses the problem of distributed multi-target tracking (MTT) in a network of sensors having different fields of view (FoVs). Probability hypothesis density (PHD) filters are running locally in every sensor for MTT. The weighted arithmetic average (WAA) fusion rule is employed to fuse the multiple local PHD densities due to its computational efficiency. First, we provide a theoretical analysis showing that the standard WAA fusion among sensors with different FoVs is unsuitable from the perspective of the principle of minimum discrimination information (PMDI). In fact, the information inconsistency among sensors due to the different FoVs unavoidably leads to an underestimation of the fused PHD. Then, motivated by the analysis, we devise two novel approaches to address the different FoV issue. The first approach accounts for the case where the sensor FoVs are known and time-invariant. The second one deals with the more complicated case where the actual sensor FoVs can be cropped due to unknown line of sight obstructions or sensors can obtain the information outside their current FoVs because of platform movement or information feedback. The essence of the proposed approaches is to perform the WAA in a more robust way by employing a set of state-dependent fusion weights which are computed online. The Gaussian mixture implementations of the proposed methods are also presented. Various simulation experiments, including a challenging tracking scenario involving six sensors with different FoVs and also random line of sight obstructions, are designed to demonstrate the efficacy of the proposed fusion approaches.

The use of moving average trading rules in Indonesian Stock Market

The development of behavioural finance stimulates the usefulness of simple trading rule. Decision biases are reflected in price movement, which is utilised by technical analysis indicators to generate trading rules. The main objective of this research is to evaluate the performance of the moving average (MA) trading rules, as a popular indicator in stock markets. The sensitivity of MA length is also examined to generate optimal return. We employ daily data of Indonesia composite index from the beginning of 1998 to the end of 2016 to examine the profitability of MA trading rule. The findings show that investors can apply MA trading rules in Indonesia stock market. Our supplementary analysis indicates that shorter (longer) MA produce higher (lower) return. The return of MA trading rules is also affected by trend. This paper contributes to trading strategy studies, particularly for stock exchanges in emerging markets. Our results recommend that selection of MA length in trading rules is important to create optimal return.

Analysis of “mixing” combination rules and Smet’s combination rule

The process of solution of different practical and ecological problems, using hyperspectral satellite images usually includes a procedure of classification. Classification is one of the most difficult and important procedures. Some image classification methods were considered and analyzed in this work. These methods are based on the theory of evidence. Evidence theory can simulate uncertainty and process imprecise and incomplete information. It were considered such combination rules in this paper: “mixing” combination rule (or averaging), convolutive x-averaging (or c-averaging) and Smet’s combination rule. It was shown, that these methods can process the data from multiple sources or spectral bands, that provide different assessments for the same hypotheses. It was noted, that the purpose of aggregation of information is to simplify data, whether the data is coming from multiple sources or different spectral bands. It was shown, that Smet’s rule is unnormalized version of Dempster rule, that applied in Smet’s Transferable Belief Model. It also processes imprecise and incomplete data. Smet’s combination rule entails a slightly different formulation of Dempster-Shafer theory. Mixing (or averaging) rule was considered in this paper too. It is the averaging operation that is used for probability distributions. This rule uses basic probability assignments from different sources (spectral bands) and weighs assigned according to the reliability of the sources. Convolutive x-averaging (or c-averaging) rule was considered in this paper too. This combination rule is a generalization of the average for scalar numbers. This rule is commutative and not associative. It also was noted, that convolutive x-averaging (c-averaging) rule can include any number of basic probability assignments. It were also considered examples, where these proposed combination rules were used. Mixing, convolutive x-averaging (c-averaging) rule and Smet’s combination rule can be applied for analysis of hyperspectral satellite images, in remote searching for minerals and oil, solving different environmental and thematic problems.

Infrared and visual image fusion based on multi-scale feature decomposition

Many applications have benefited from infrared (IR) and visual (VI) image fusion technology. To integrate more useful information from the source images, in this paper, we propose a novel IR and VI image fusion method based on multi-scale feature decomposition (MSFD). The main advantage of MSFD is that it separates image features more effectively. Firstly, MSFD is introduced to decompose the source images into base layers and several detail (feature) layers. Then, the average fusion rule is applied to fuse the decomposed base layers to ensure the robustness of the proposed method, and the sum fusion rule is used to fuse the decomposed feature layers to ensure that more feature information can be transferred from the source images to the fusion result. Finally, the fusion result is reconstructed by adding the fused base layer and the fused feature layers. Experimental results demonstrate the advantages of our proposed method over other compared methods in term of visual observation and evaluation metrics.

Discrete Wavelet Transform Based Image Fusion Using Unsharp Masking

Nowadays the result of infrared and visible image fusion has been utilized in significant applications like military, surveillance, remote sensing and medical imaging applications. Discrete wavelet transform based image fusion using unsharp masking is presented. DWT is used for decomposing input images (infrared, visible). Approximation and detailed coefficients are generated. For improving contrast unsharp masking has been applied on approximation coefficients. Then for merging approximation coefficients produced after unsharp masking average fusion rule is used. The rule that is used for merging detailed coefficients is max fusion rule. Finally, IDWT is used for generating a fused image. The result produced using the proposed fusion method is providing good contrast and also giving better performance results in reference to mean, entropy and standard deviation when compared with existing techniques.

Trip Extraction of Shared Electric Bikes Based on Multi-Rule-Constrained Homomorphic Linear Clustering Algorithm

Trajectory data include rich interactive information of humans. The correct identification of trips is the key to trajectory data mining and its application. A new method, multi-rule-constrained homomorphic linear clustering (MCHLC), is proposed to extract trips from raw trajectory data. From the perspective of the workflow, the MCHLC algorithm consists of three parts. The first part is to form the original sub-trajectory moving/stopping clusters, which are obtained by sequentially clustering trajectory elements of the same motion status. The second part is to determine and revise the motion status of the original sub-trajectory clusters by the speed, time duration, directional constraint, and contextual constraint to construct the stop/move model. The third part is to extract users’ trips by filtering the stop/move model using the following rules: distance rule, average speed rule, shortest path rule, and completeness rule, which are related to daily riding experiences. Verification of the new method is carried out with the shared electric bike trajectory data of one week in Tengzhou city, evaluated by three indexes (precision, recall, and F1-score). The experiment shows that the index values of the new algorithm are higher (above 93%) than those of the baseline methods, indicating that the new algorithm is better. Compared to the baseline velocity sequence linear clustering (VSLC) algorithm, the performance of the new algorithm is improved by approximately 10%, mainly owing to two factors, directional constraint and contextual constraint. The better experimental results indicate that the new algorithm is suitable to extract trips from the sparse trajectories of shared e-bikes and other transportation forms, which can provide technical support for urban hotspot detection and hot route identification.

Average Inflation Targeting and Interest-Rate Smoothing

We study the welfare implication of average inflation targeting as a simple interest-rate rule, in which the monetary authority adjusts its short-term policy rate in response to the output gap as well as average inflation deviation from its target instead of reacting to the contemporaneous inflation rate as in a Taylor-type rule. We find that the welfare improvement achieved by switching to average inflation targeting from a standard Taylor rule is modest with a high degree of interest-rate smoothing, whereas it is significant without interest-rate smoothing. We show that average inflation targeting is welfare-improving in the same way as interest-rate smoothing by making the conduct of monetary policy history-dependent. Thus, the high degree of monetary policy inertia in the estimated interest-rate rules in many advanced economies implies that the welfare gain from adopting the average inflation targeting rule would be modest.

Optimized Incremental Mining of Customer Buying Behavior using Temporal Association Rules

In retail business, customers’ behavior analytics is a study of customers’ buying behavior for a better understanding of customer needs to be able to provide service accordingly. The buying behavior is majorly influenced by the preferences of a customer. However, preferences of a customer change over a period of time due to various factors like change in income, taste, culture or newer products, etc. Understanding these changes in customer behavior is a very challenging task especially in a dynamic, ever-changing environment. There are various customer behavior mining models and techniques available in the data mining domain that are designed to work on static and dynamic databases. The traditional incremental mining techniques consider all the previous datasets in order to update the patterns. However, in a dynamic database, the size of the database grows with every update. To mine customers’ behavior in a time-variant database, the re-mining of the updated database is required that further increases processing cost in terms of execution time and memory space with every update. The purpose of this paper is to propose a method that can analyze the changes in customers’ behavior in time-variant databases without mining all the transactions. In this paper, an optimized incremental technique is proposed that utilizes temporal association rule mining in a time-variant database for mining customer behavioral patterns in an updated database. The proposed algorithm named ‘Autoregressive Moving Average model-based Incremental Temporal Association Rules Mining (ARMA-ITARM)’ utilizes the ARMA model to substantially reduce the database and maintains temporal frequent patterns in the updated database. Inspired by sliding window and pre-large concepts, the algorithm utilizes past frequent itemsets and probable frequent itemsets from customers’ purchased history along with frequent itemsets and probable frequent itemsets that reduce search space. Consequently, the entire database is scanned only once to count the frequency of occurrence of a few candidate itemsets. In effect, execution time memory need of the algorithm is very small. Experimental results demonstrate that our proposed technique performs better over recent techniques like ITARM, SWF, etc.

A cointegrating stock trading strategy: application to listed tanker shipping companies

In the current paper, we propose a strategy to trade a portfolio of listed shipping companies in the US market. In particular, we estimate a co-integrating relationship between the weekly stock market returns of a portfolio of tanker shipping companies and the Baltic Tanker Index, exploiting the close relationship between freight rates and the stock market performance of shipping companies. Our results suggest that a trading strategy on the basis of a co-integrating relationship and a simple moving average rule outperforms, by approximately 50%, a standard buy-and-hold strategy in various investment horizons, often by a very wide margin. Given the latter, the results allow us to enhance the current literature on shipping finance by providing evidence of how simple investment strategies can benefit both retail and institutional investors who do not have direct exposure or experience in the shipping industry by allowing them to include shipping stocks in their portfolios.

A Cointegrating Stock Trading Strategy for Tanker Shipping Companies

We propose a strategy to trade a portfolio of listed shipping companies in the US market. In particular, we estimate a cointegrating relationship between the weekly stock market returns of a portfolio of tanker shipping companies and the Baltic Tanker Index, exploiting the close relationship between freight rates and the stock market performance of shipping companies. Our results suggest that a trading strategy on the basis of a cointegrating relationship and a simple moving average rule outperforms a standard buy-and-hold strategy in various investment horizons, often by a very wide margin.

Adaptive rules for discrete-time Cournot games of high competition level markets

For the Cournot-like oligopoly games of n firms, where the Cournot adjustment fails to converge, we propose adjustment processes originating from the family of the Moving Averages. In markets of linear demand, where firms have private and linear on quantities cost functions, these adaptive rules turn the games into discrete-time linear systems with delays. With an out of the box proof, we determine the least number of delays (m) that ensures the game of n players converges to its equilibrium. The Simple Moving Average rule (fixed number of delays) and the Cumulative Moving Average rule (constantly increasing number of delays), which is also known as “fictitious play”, are the main rules considered. Along with a hybrid rule, result of their combination, they are all studied for their convergent properties and compared in a bench-marking framework to indicate the different trajectories and identify their suitability in applications.

Nuclear physics aspects of the GW170817 neutron star merger event

The detection of the GW170817 neutron star merger event by the Advanced LIGO and Virgo collaborations has stimulated an intense research activity regarding novel aspects of nuclear physics, which may play an essential role in the understanding of the merger dynamics. Among those, one of the most relevant regards the nuclear-matter equation of state. The analysis of the gravitational wave signal has shown that the upper limit on the average tidal deformability rules out very stiff equations of state, whereas a lower limit can be extracted from the analysis of the kilonova AT2017gfo signal. Those limits translate into an allowed range for the radius R1.5 of a 1.5 solar mass neutron star, 11.8 km ≲ R1.5 ≲ 13.1 km. On the other hand, radii significantly smaller than 12 km have been suggested by X-ray observations. This clash can be solved if the event GW170817/AT2017gfo would originate from the merger of a hadronic star and a star containing quark matter, as in the two-families and in the twin-stars scenarios.

Algorithm for Neutrosophic Soft Sets in Stochastic Multi-Criteria Group Decision Making Based on Prospect Theory

To address issues involving inconsistencies, this paper proposes a stochastic multi-criteria group decision making algorithm based on neutrosophic soft sets, which includes a pair of asymmetric functions: Truth-membership and false-membership, and an indeterminacy-membership function. For integrating an inherent stochastic, the algorithm expresses the weights of decision makers and parameter subjective weights by neutrosophic numbers instead of determinate values. Additionally, the algorithm is guided by the prospect theory, which incorporates psychological expectations of decision makers into decision making. To construct the prospect decision matrix, this research establishes a conflict degree measure of neutrosophic numbers and improves it to accommodate the stochastic multi-criteria group decision making. Moreover, we introduce the weighted average aggregation rule and weighted geometric aggregation rule of neutrosophic soft sets. Later, this study presents an algorithm for neutrosophic soft sets in the stochastic multi-criteria group decision making based on the prospect theory. Finally, we perform an illustrative example and a comparative analysis to prove the effectiveness and feasibility of the proposed algorithm.

Fusion-based encryption scheme for cancelable fingerprint recognition

This paper presents a fingerprint image encryption scheme based on fingerprint image fusion with another visible image that is rich in details. The encryption process is performed with chaotic Baker map, which has large immunity to noise. The image fusion process is performed with the Haar wavelet transform, and it can be implemented with the average or maximum fusion rule. The fusion process is performed, because fingerprint images are not rich in details, and hence the direct application of chaotic Baker map encryption will not be efficient for encrypting this type of images. To obtain an image that is rich in details, it is possible to use another encrypted image with a strong ciphering algorithm such as the RC6. Several perspectives are considered for performance evaluation of the proposed encryption scheme including visual inspection, histogram analysis, correlation coefficient, entropy analysis, processing time, and the effect of noise after decryption. The proposed fingerprint encryption scheme is appropriate for cancelable biometric applications to preserve the privacy of users by keeping their original fingerprints away from usage in the recognition system. The simulation results demonstrate that the proposed image encryption scheme gives a proficient and secure path for unique encrypted fingerprints. Both Equal Error Rate (EER) and Area under Receiver Operating Characteristic (AROC) curve are used for performance evaluation of the proposed cancelable fingerprint recognition scheme revealing high performance.

The effectiveness of technical trading rules in cryptocurrency markets

We analyse various technical trading rules in the form of the moving average-oscillator and trading range break-out strategies to specifically test resistance and support levels and their trading performance using high-frequency Bitcoin returns. Overall, our results provide significant support for the moving average strategies. In particular, variable-length moving average rule performs the best with buy signals generating higher returns than sell signals.

Theoretical and practical motivations for the use of the moving average rule in the stock market

Abstract This paper provides some theoretical foundations for using moving average (MA) rules in the stock market. In particular, the paper analyzes the conditional probability of price increments and examines how this probability varies over time. We prove under certain assumptions that the probability of being in an uptrend is greater than the probability of being in a downtrend. This demonstration partially justifies the common use of MA rules in the stock market. Finally, we propose an ex-post empirical analysis to evaluate and compare the performance of some MA rules and other portfolio strategies in the US stock market. In this context, we also suggest a methodology that incorporates these trading rules as alarm rules to predict potential market failures. Our ex-post results confirm the advantages of using these trading rules to predict market trends and crises.

Internality of generalized averaged Gaussian quadrature rules and truncated variants for measures induced by Chebyshev polynomials

Generalized averaged Gaussian quadrature rules and truncated variants associated with a nonnegative measure with support on a real open interval {t:a<t<b} may have nodes outside this interval, in other words the rules may fail to be internal. Such rules cannot be applied when the integrand is defined on {t:a<t<b} only. This paper investigates whether generalized averaged Gaussian quadrature rules and truncated variants are internal for measures induced by Chebyshev polynomials. Our results complement those of Notaris [13] for Gauss-Kronrod quadrature formulas for the same kind of measures.