• The Journal of Asian Finance, Economics and Business
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• v.9 no.10
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• pp.1-13
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• 2022

This paper empirically explores the predicting ability of the newly proposed smooth transition (ST) time-varying combining forecast methods. The proposed method allows the "weight" of combining forecasts to change gradually over time through its unique feature of transition variables. Stock market returns from 7 countries were applied to Ad Hoc models, the well-known Generalized Autoregressive Conditional Heteroskedasticity (GARCH) family models, and the Smooth Transition Exponential Smoothing (STES) models. Of the individual models, GJRGARCH and STES-E&AE emerged as the best models and thereby were chosen for constructing the combined forecast models where a total of nine ST combining methods were developed. The robustness of the ST combining forecasts is also validated by the Diebold-Mariano (DM) test. The post-sample forecasting performance shows that ST combining forecast methods outperformed all the individual models and fixed weight combining models. This study contributes in two ways: 1) the ST combining methods statistically outperformed all the individual forecast methods and the existing traditional combining methods using simple averaging and Bates & Granger method. 2) trading volume as a transition variable in ST methods was superior to other individual models as well as the ST models with single sign or size of past shocks as transition variables.

• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.175-182
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• 2020

Fisher's classical method for combining independent p-values from continuous distributions is widely used but it is known to be inadequate for combining p-values from discrete probability distributions. Instead, the discrete analog of Fisher's classical method is used as an alternative for combining p-values from discrete distributions. In this paper, firstly we obtain p-values from discrete probability distributions associated with multi-sample location test data (Fisher-Pitman test and Kruskall-Wallis test data) by permutation method, and secondly combine the permutaion p-values by the discrete analog of Fisher's classical method. And we finally compare the combined p-values from both the discrete analog of Fisher's classical method and Fisher's classical method.

• Journal of the Korea Society of Computer and Information
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• v.23 no.7
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• pp.99-104
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• 2018

In this paper, we compare Fisher's continuous method with an exact discrete analog of Fisher's continuous method from permutation tests for combining p values. The discrete analog of Fisher's continuous method is known to be adequate for combining independent p values from discrete probability distributions. Also permutation tests are widely used as alternatives to conventional parametric tests since these tests are distribution-free, and yield discrete probability distributions and exact p values. In this paper, we obtain permutation p values from discrete probability distributions using Mann-Whitney test data sets (real data and hypothetical data) and combine p values by the exact discrete analog of Fisher's continuous method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.2
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• pp.281-288
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• 2004

In this paper, we consider the diversity combining method for multiple symbol differential detection (MSDD) over the slow fading diversity channel. Though the performance of the optimum maximum-likelihood sequence estimator for MSDD approaches the performance of maximal-ratio combining with differential encoding, the complexity increases exponentially as the size of MSDD block is increased. This new pre-combining method can make use of the efficient MSDD algorithm that has a complexity increasing linearly with the block length or MSDD. Thus, in many wireless scenarios where it is not possible to perform coherent detection. this pre-combined diversity MSDD can be applied to obtain substantial gain compare to conventional differential detection.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1949-1952
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• 2004

GPS is widely used for outdoor positioning in many applications. But it is not suitable for positioning in an obstacle environment such as urban area, tunnels and so on, due to variable signal level. So new technology of the positioning is required to provide the consistent error level regardless of any changes in any environment. Abrupt changes of GPS signal can be detected by various fault detection and isolation methods. Conventional FDI (Fault Detection and Isolation) methods are categorized into two approaches. One approach is the snapshot method that uses measurements only at present step. The other approach is the filtering method that uses measurements stacked from previous step to present step. The FDI result of the snapshot method can be considered reliable independently with previous results and the FDI result of the filtering method is more reliable and detection time is a little longer. Therefore combining approach of two methods is proposed for increasing FDI performance in this paper. Three approaches that are the snapshot method, the filtering method and the combining method are compared to show the probability of correct FDI in simulations. The combining approach presents best result of FDI among them and shows the consistent accuracy irrespective of any changes in outdoor environment.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.3
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• pp.193-199
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• 2000

In many studies, considerable attention has been focussed upon choosing a model which represents underlying process of time series and forecasting the future. In the real world, however, there may be some cases that one model can not reflect all the characteristics of original time series. Under such circumstances, we may get better performance by combining the forecasts from several models. The most popular methods for combining forecasts involve taking a weighted average of multiple forecasts. But the weights are usually unstable. In cases the assumptions of normality and unbiasedness for forecast errors are satisfied, a Bayesian method can be used for updating the weights. In the real world, however, there are many circumstances the Bayesian method is not appropriate. This paper proposes a PNN(Probabilistic Neural Net) approach as a method for combining forecasts that can be applied when the assumption of normality or unbiasedness for forecast errors is not satisfied. In this paper, PNN method, which is similar to Bayesian approach, is suggested as an updating method of the unstable weights in the combination of the forecasts. The PNN method has been usually used in the field of pattern recognition. Unlike the Bayesian approach, it requires no assumption of a specific prior distribution because it gets probabilities by using the distribution estimated from given data. Empirical results reveal that the PNN method offers superior predictive capabilities.

• Journal of Satellite, Information and Communications
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• v.12 no.2
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• pp.38-41
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• 2017

Reception performance in land mobile satellite is decreased by obstacle. It is compensated with Diversity technique. In this paper, performances are analyzed with two type of method Equal Gain Combining(EGC) and Selcetive Combining(SC). To analyze, measured data using On-The-Move(OTM) terminal are used. In conclusion, SC method can increase performance. However, EGC method can improve perforamance only in rural region, but performance are decreased in urban region.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.5
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• pp.611-619
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• 2014

Combining similar data provides larger data sets through conducting test for homogeneity of several samples under various production processes or samples from different LOTs. The test for homogeneity has been applied to either variable or attribute data, and for variable data set physical homogeneity has been tested without consideration of the specification to the set. This paper proposes a method for test of homogeneity based on quality level through using both variable data and the specification. Quality-based test for homogeneity as a way of combining data is implemented by test for coefficient of variation in the proposed method. The method was verified through the application to the data set in open literature. And possibility to combine performance data for various types of thermal battery was discussed in order to estimate operation reliability.

• Journal of the Korea Safety Management & Science
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• v.18 no.4
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• pp.161-169
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• 2016

In this research, we propose efficient demand forecasting scheme for intermittent demand. For this purpose, we first extensively analyze the drawbacks of the existing forecasting methods such as Croston method and Syntetos-Boylan approximation, then using these findings we propose the new demand forecasting method. Our goal is to develop forecasting method robust across many situations, not necessarily optimal for a limited number of specific situations. For this end, we adopt combining forecasting method that utilizes unbiased forecasting methods such as simple exponential smoothing and simple moving average. Various simulation results show that the proposed forecasting method performed better than the existing forecasting methods.

• Journal of Broadcast Engineering
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• v.11 no.2 s.31
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• pp.222-228
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• 2006

In this paper, we propose a receive diversity method for orthogonal frequency division multiplexing (OFDM) systems with cochannel interference. In the method, combining is done in the frequency domain by using the subcarrier based maximum ratio combining (MRC) method. For MRC, we exploit the power of cochannel interference as well as the power of channel noise. The accuracy of the power estimate of interference plus noise is enhanced by averaging the initial estimates over the correlated subchannels where the coherency between the subchannel gains comes from the limited delay spread of the channel. Simulation results show that the proposed method yields 2-3.5dB gain of signal to noise ratio compared to the conventional MRC method and less than 1 dB difference to the ideal case.