• Journal of Distribution Science
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• v.16 no.9
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• pp.53-58
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• 2018

Purpose - This study aims to examine how the time variations of customer satisfaction influence retail firms' performance. Research design, data, and methodology - The study employs yearly time series customer satisfaction data of Korean retail secured from the National Customer Satisfaction Index(NCSI) for the 2011~2016 period. Our data includes a total of 90 observations of 15 retail firms in 5 different sector(department store, filling station, large discount store, open market, TV home shopping). We obtained the firm performance data from the KIS Value database. The variables for financial performance include sales and net profit. Results - The results show that customer satisfaction has dynamic effects on retail firms' performance. More specifically, the time variation of customer satisfaction has the moderating effect on the linkage between customer satisfaction and financial performance as well as direct effects on the firms' financial performance. Conclusions - Customer satisfaction has the current effect lasting over time on firm performance and changes of customer satisfaction in positive direction also impact on firm performance. Retail firms need to not only focus on improving customer satisfaction in the current term, but make efforts to continuously enhance customer satisfaction in the long term.

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.319-330
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• 2006

A signal in real world usually composes of multiple signals having different scales of frequencies. For example sun-spot data is fluctuated over 11 year and 85 year. Economic data is supposed to be compound of seasonal component, cyclic component and long-term trend. Decomposition of the signal is one of the main topics in time series analysis. However when the signal is subject to nonstationarity, traditional time series analysis such as spectral analysis is not suitable. Huang et. at(1998) proposed data-adaptive method called empirical mode decomposition (EMD) . Due to its robustness to nonstationarity, EMD has been applied to various fields. Huang et. at, however, have not considered denoising when data is contaminated by error. In this paper we propose efficient denoising method utilizing cross-validation.

• Journal of Astronomy and Space Sciences
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• v.25 no.4
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• pp.395-404
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• 2008

We explore the linkage between the long term variability of the Sun and earth's climate change by analysing periodicities of time series of solar proxies and global temperature anomalies. We apply the power spectral estimation method named as the periodgram to solar proxies and global temperature anomalies. We also decompose global temperature anomalies and reconstructed total solar irradiance into each local variability components by applying the EMD (Empirical Mode Decomposition) and MODWT MRA (Maximal Overlap Discrete Wavelet Multi Resolution Analysis). Powers for solar proxies at low frequencies are lower than those of high frequencies. On the other hand, powers for temperature anomalies show the other way. We fail to decompose components which having lager than 40 year variabilities from EMD, but both residuals are well decomposed respectively. We determine solar induced components from the time series of temperature anomalies and obtain 39% solar contribution on the recent global warming. We discuss the climate system can be approximated with the second order differential equation since the climate sensitivity can only determine the output amplitude of the signal.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.55 no.3
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• pp.264-272
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• 2019

The aim of this study is to analyze the relationship between international oil price as a fuel cost in overseas fisheries and skipjack tuna price as a part of main products in overseas fisheries using monthly time series data from 2008 to 2017. The study also tried to analyze the change of fishing profits by fuel cost. For a time series analysis, this study conducted both the unit-root test for stability of data and the Johansen cointegration test for long-term equilibrium relations among variables. In addition, it used not only the Granger causality test to examine interactions among variables, but also the Vector Auto Regressive (VAR) model to estimate statistical impacts among variables used in the model. Results of this study are as follows. First, each data on variables was not found to be stationary from the ADF unit-root test and long-term equilibrium relations among variables were not found from a Johansen cointegration test. Second, the Granger causality test showed that the international oil prices would directly cause changes in skipjack tuna prices. Third, the VAR model indicated that the posterior t-2 period change of international oil price would have an statistically significant effect on changes of skipjack tuna prices. Finally, fishing profits from skipjack would be decreased by 0.06% if the fuel cost increases by 1%.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.3
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• pp.123-128
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• 2019

This paper proposes the stock price prediction based on the artificial intelligence, where the model with recurrent convolution neural network (RCNN) layers is adopted. In the motivation of this prediction, long short-term memory model (LSTM)-based neural network can make the output of the time series prediction. On the other hand, the convolution neural network provides the data filtering, averaging, and augmentation. By combining the advantages mentioned above, the proposed technique predicts the estimated stock price of next day. In addition, in order to emphasize the recent time series, a custom weighted loss function is adopted. Moreover, stock data related to the stock price index are adopted to consider the market trends. In the experiments, the proposed stock price prediction reduces the test error by 3.19%, which is over other techniques by about 19%.

• Journal of Korea Trade
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• v.25 no.4
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• pp.17-36
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• 2021

Purpose - This study provides useful information to stakeholders by forecasting the tramp shipping market, which is a completely competitive market and has a huge fluctuation in freight rates due to low barriers to entry. Moreover, this study provides the most effective parameters for Baltic Dry Index (BDI) prediction and an optimal model by analyzing and comparing deep learning models such as the artificial neural network (ANN), recurrent neural network (RNN), and long short-term memory (LSTM). Design/methodology - This study uses various data models based on big data. The deep learning models considered are specialized for time series models. This study includes three perspectives to verify useful models in time series data by comparing prediction accuracy according to the selection of external variables and comparison between models. Findings - The BDI research reflecting the latest trends since 2015, using weekly data from 1995 to 2019 (25 years), is employed in this study. Additionally, we tried finding the best combination of BDI forecasts through the input of external factors such as supply, demand, raw materials, and economic aspects. Moreover, the combination of various unpredictable external variables and the fundamentals of supply and demand have sought to increase BDI prediction accuracy. Originality/value - Unlike previous studies, BDI forecasts reflect the latest stabilizing trends since 2015. Additionally, we look at the variation of the model's predictive accuracy according to the input of statistically validated variables. Moreover, we want to find the optimal model that minimizes the error value according to the parameter adjustment in the ANN model. Thus, this study helps future shipping stakeholders make decisions through BDI forecasts.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.131-131
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• 2021

Deep learning models, especially those based on long short-term memory (LSTM), have presented their superiority in addressing time series data issues recently. This study aims to comprehensively evaluate the performance of deep learning models that belong to the supervised learning category in streamflow prediction. Therefore, six deep learning models-standard LSTM, standard gated recurrent unit (GRU), stacked LSTM, bidirectional LSTM (BiLSTM), feed-forward neural network (FFNN), and convolutional neural network (CNN) models-were of interest in this study. The Red River system, one of the largest river basins in Vietnam, was adopted as a case study. In addition, deep learning models were designed to forecast flowrate for one- and two-day ahead at Son Tay hydrological station on the Red River using a series of observed flowrate data at seven hydrological stations on three major river branches of the Red River system-Thao River, Da River, and Lo River-as the input data for training, validation, and testing. The comparison results have indicated that the four LSTM-based models exhibit significantly better performance and maintain stability than the FFNN and CNN models. Moreover, LSTM-based models may reach impressive predictions even in the presence of upstream reservoirs and dams. In the case of the stacked LSTM and BiLSTM models, the complexity of these models is not accompanied by performance improvement because their respective performance is not higher than the two standard models (LSTM and GRU). As a result, we realized that in the context of hydrological forecasting problems, simple architectural models such as LSTM and GRU (with one hidden layer) are sufficient to produce highly reliable forecasts while minimizing computation time because of the sequential data nature.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.1
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• pp.1-8
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• 2024

This study analyzed the impact of Higher-order resources on profit sustainability for domestic companies using a mathematical statistical model. Higher-order resources refer to resources that do not directly affect profits but influence other resources that directly contribute to profits. As a result of analysis using 30 years of actual data from more than 650 domestic companies, the average duration of competitive advantage including high-order resources was found to be about twice as long as the period suggested by the autoregressive model excluding high-order resources. Through this, if companies want to earn more profits over a long period of time than their competitors, they must not only possess resources that are more valuable, rare, difficult to imitate, and non-substitutable compared to their competitors, but also that higher-order resources can contribute to changes in these resources over time. It was confirmed that it must lead the long-term profit difference. High-level resources include strategic planning, mergers and acquisitions (M&A) capabilities, and good forecasting.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.877-888
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• 2017

Spectral analysis is used to determine the frequency of time series data. We first determine the frequency of the series through the power spectrum or the periodogram and then calculate the period of a cycle that may exist in a time series. Estimating the frequency using a Bayesian technique has been developed and proven to be useful; however, the Bayesian estimator for the frequency cannot be analytically solved through mathematical equations and may be handled numerically or computationally. In this paper, we make an inference on the Bayesian frequency through both resampling a parameter by Markov chain Monte Carlo (MCMC) methods and resampling data by bootstrap methods for a time series. We take the Korean real estate price index as an example for Bayesian frequency estimation. We have found a difference in the periods between the sale price index and the long term rental price index, but the difference is not statistically significant.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.133-144
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• 2016

This paper proposes a parsimonious periodic autoregressive (PAR) model. The proposed model performance is evaluated through an analysis of Korean unemployment rate series that is compared with existing models. We exploit some common features among each seasonality and confirm it by LR test for the parsimonious PAR model in order to impose a parsimonious structure on the PAR model. We observe that the PAR model tends to be superior to existing seasonal time series models in mid- and long-term forecasts. The proposed parsimonious model significantly improves forecasting performance.