• 유통과학연구
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• 제14권1호
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• pp.93-102
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• 2016

Purpose - The purpose of paper is studying the static and dynamic side for long-term memory storage properties, and increase the explanatory power regarding the long-term memory process by looking at the long-term storage attributes, Korea Composite Stock Price Index. The reason for the use of GPH statistic is to derive the modified statistic Korea's stock market, and to research a process of long-term memory. Research design, data, and methodology - Level shifts were subjected to be an empirical analysis by applying the GPH method. It has been modified by taking into account the daily log return of the Korea Composite Stock Price Index a. The Data, used for the stock market to analyze whether deciding the action by the long-term memory process, yield daily stock price index of the Korea Composite Stock Price Index and the rate of return a log. The studies were proceeded with long-term memory and long-term semiparametric method in deriving the long-term memory estimators. Chapter 2 examines the leading research, and Chapter 3 describes the long-term memory processes and estimation methods. GPH statistics induced modifications of statistics and discussed Whittle statistic. Chapter 4 used Korea Composite Stock Price Index to estimate the long-term memory process parameters. Chapter 6 presents the conclusions and implications. Results - If the price of the time series is generated by the abnormal process, it may be located in long-term memory by a time series. However, test results by price fixed GPH method is not followed by long-term memory process or fractional differential process. In the case of the time-series level shift, the present test method for a long-term memory processes has a considerable amount of bias, and there exists a structural change in the stock distribution market. This structural change has implications in level shift. Stratum level shift assays are not considered as shifted strata. They exist distinctly in the stock secondary market as bias, and are presented in the test statistic of non-long-term memory process. It also generates an error as a long-term memory that could lead to false results. Conclusions - Changes in long-term memory characteristics associated with level shift present the following two suggestions. One, if any impact outside is flowed for a long period of time, we can know that the long-term memory processes have characteristic of the average return gradually. When the investor makes an investment, the same reasoning applies to him in the light of the characteristics of the long-term memory. It is suggested that when investors make decisions on investment, it is necessary to consider the characters of the long-term storage in reference with causing investors to increase the uncertainty and potential. The other one is the thing which must be considered variously according to time-series. The research for price-earnings ratio and investment risk should be composed of the long-term memory characters, and it would have more predictability.

• 무역학회지
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• 제46권6호
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• pp.191-209
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• 2021

In this paper, various time series analysis models and machine learning models are presented for long-term prediction of export growth rate, and the prediction performance is compared and reviewed by RMSE and MAE. Export growth rate is one of the major economic indicators to evaluate the economic status. And It is also used to predict economic forecast. The export growth rate may have a negative (-) value as well as a positive (+) value. Therefore, Instead of using the ReLU function, which is often used for time series prediction of deep learning models, the PReLU function, which can have a negative (-) value as an output value, was used as the activation function of deep learning models. The time series prediction performance of each model for three types of data was compared and reviewed. The forecast data of long-term prediction of export growth rate was deduced by three forecast methods such as a fixed forecast method, a recursive forecast method and a rolling forecast method. As a result of the forecast, the traditional time series analysis model, ARDL, showed excellent performance, but as the time period of learning data increases, the performance of machine learning models including LSTM was relatively improved.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제8권7호
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• pp.289-302
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• 2019

시계열의 구조 변화란, 전체 시계열 자료를 구성하는 기간에서 관측치들의 분포가 상대적으로 안정적이다가, 특정 시점에서 분포 특성의 급격한 변화를 보이는 것을 의미한다. 비정상(non-stationary) 장기 시계열 안에서도, 단기적인 추세의 변화가 일시적인 것인지, 아니면 구조적으로 변한 것인지를 적시에 판단하는 것은 중요하다. 이는 시계열 추세의 변화를 상시 감지하여, 변화에 맞는 적정한 대응을 할 필요가 있기 때문이다. 본 연구에서는 단위근 검정법을 기반으로 한 검정 결과를 시각화함으로써, 의사결정자가 시계열의 구조 변화를 손쉽게 파악할 수 있는 방안을 제시하였다. 특히 시계열을 분할한 후 검정하는 방법을 통해, 장기 시계열일 때에도 단기 구조 변화를 파악할 수 있도록 하였다.

• 한국컴퓨터정보학회논문지
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• 제28권2호
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• pp.201-207
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• 2023

본 논문에서는 Multi-Step Time Series의 세 가지 전략을 비교 분석하기 위해 LGBM, MLP, LSTM, GRU를 사용하여 농산물 중장기 가격 예측에 대한 최적의 모형을 제안한다. 제안 모형은 다각도로 전략을 선택하여 모델과 전략간 최적의 조합을 찾도록 설계되었다. 기존 농산물 가격 예측 연구에서는 전통 계량경제 모델인 ARIMA를 비롯하여 LSTM 계열 모델이 주로 사용된 반면 Multi-Step Time Series 관련 농산물 가격 예측 연구는 매우 제한적이다. 본 연구에서는 농산물 가격의 변동성 정도에 따라 두 개의 기간으로 나누어 실험을 진행하였으며, Direct, Hybrid, Multiple Outputs 등 세 전략의 중장기 가격 예측 결과 Hybrid 접근법이 상대적으로 우수한 성능을 보였다.본 연구 결과는 중장기 일별 가격 예측을 고도화할 수 있는 효과적인 대안을 제시한다는 측면에서 학술적, 실무적 의의를 갖는다.

• 품질경영학회지
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• 제47권3호
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• pp.497-508
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• 2019

Purpose: The purpose of this study is to overcome limitations of conventional studies that to predict Baltic Dry Index (BDI). The study proposed applications of Artificial Neural Network (ANN) named Long Short-Term Memory (LSTM) to predict BDI. Methods: The BDI time-series prediction was carried out through eight variables related to the dry bulk market. The prediction was conducted in two steps. First, identifying the goodness of fitness for the BDI time-series of specific ANN models and determining the network structures to be used in the next step. While using ANN's generalization capability, the structures determined in the previous steps were used in the empirical prediction step, and the sliding-window method was applied to make a daily (one-day ahead) prediction. Results: At the empirical prediction step, it was possible to predict variable y(BDI time series) at point of time t by 8 variables (related to the dry bulk market) of x at point of time (t-1). LSTM, known to be good at learning over a long period of time, showed the best performance with higher predictive accuracy compared to Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN). Conclusion: Applying this study to real business would require long-term predictions by applying more detailed forecasting techniques. I hope that the research can provide a point of reference in the dry bulk market, and furthermore in the decision-making and investment in the future of the shipping business as a whole.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2005년도 학술발표회 논문집
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• pp.792-796
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• 2005

The methods of times series analysis have been recognized as important tools for assisting in solving problems related to the management of water resources. Especially, After more than 40 years the so-called Hurst effect remains an open problem in stochastic hydrology. Until now, its existence has been explained fly R/S analysis that roots in early work of the British hydrologist H.E. Hurst(1951). Today, the Hurst analysis is mostly used for the hydrological studies for memory and characteristics of time series and many methodologies have been developed for the analysis. So, there are many different techniques for the estimation of the Hurst exponent(H). However, the techniques can produce different characteristics for the persistence of a time series each other. We found that DFA is the most appropriate technique for the Hurst exponent estimation for both the shot term memory and long term memory. We analyze the SOI(Southern Oscillations Index) and 6 tree-ring series for USA sites by means of DFA and the BDS statistic is used for nonlinearity test of the series. From the results, we found that SOI series is nonlinear time series which has a long term memory of H=0.92. Contrary to earlier work of Rao(1999), all the tree- ring series are not random from our analysis. A certain tree ring series show a long term memory of H=0.97 and nonlinear property. Therefore, we can say that the SOI and tree-ring series may show long memory and nonlinearity.

• 산업공학
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• 제16권4호
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• pp.400-410
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• 2003

Most of the demand forecasting which have been studied is about long-term time series over 15 years demand forecasting. In this paper, we set up the most optimal ARIMA model for the short-term time series demand forecasting and suggest demand forecasting system for short-term time series by appraising suitability and predictability. We are going to use the univariate ARIMA model in parallel with the bivariate transfer function model to improve the accuracy of forecasting. We also analyze the effect of advertisement cost, scale of branch stores, and number of clerk on the establishment of marketing policy by applying statistical methods. After then we are going to show you customer's needs, which are number of buying products. We have applied this method to forecast the annual sales of refrigerator in four branch stores of A company.

• 재무관리연구
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• 제25권3호
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• pp.111-130
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• 2008

이 논문은 분수공적분 개념을 이용하여 KOSPI200지수와 지수선물가격간에 장기균형관계가 있는지를 살펴보고 있다. 이것을 위해 로그변환 현 선물가격 각각의 분수차분계수를 주파수영역 (frequency domain)의 GPH 추정량을 구한 다음, 현 선물 회귀식의 추정을 통해 도출한 균형오차의 차분계수와 비교하였다. 이 방법은 전통적인 공적분방법에서 규명하지 못한 금융시계열자료의 통계적인 특성을 분석할 수 있는 장점이 있다. 분석결과를 요약하면 다음과 같다. 첫째, 정수차원의 차분구조모형에서는 공적분검정을 통한 장기균형관계의 증거를 찾기가 어려웠다. ADF 단위근 검정과 KPSS 정상성 검정에서 상반된 결과가 제시되어 두 시계열을 I(1)으로 확정하기가 불가능하였다. 둘째, GPH 추정량를 이용하여 차분계수를 추정한 결과, 두 시계열 모두 불안정한 장기기억구조를 가지는 것으로 식별되었고 균형오차는 정상적인(stationary) 장기기억구조를 가져 현 선물가격간에 분수공적분관계가 있는 것으로 파악되었다. 이 논문은 선물시장과 현물시장이 장기균형관계를 국내 선행연구에서 이용하지 않았던 분수공적분을 이용하여 분석했다는 점에서 그 의의를 찾을 수 있다.

• 한국음향학회지
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• 제42권4호
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• pp.313-319
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• 2023

본 논문에서는 일부 소음 데이터만 알고 있을 때 결손된 데이터를 예측할 목적으로 수조에서 측정된 기포유동 소음 데이터와 수중 운동체 발사 소음 데이터를 시계열 기계학습 모델인 Long Short Term Memory(LSTM)에 적용해 보았다. 기포유동소음 데이터는 파이프에서 측정된 소음으로 기포소음, 유동소음, 유체기인소음이 혼합되어 있으며 유형별로 3가지로 분류할 수 있다. 수중 운동체 발사소음은 모형 발사튜브에서 수중 운동체가 사출될 때 발생하는 소음으로 순간소음이며 발사 이벤트마다 불규칙하게 변한다. 이러한 종류의 소음 생성을 위해서는 해석적인 모델보다는 데이터 기반 모델이 유용할 수 있다. 본 연구에서는 LSTM을 데이터 기반 모델을 만들었다. 모델에 영향을 주는 LSTM의 은닉유닛의 개수, 입력시퀸스의 개수, 데시메이션 인자에 따른 모델의 성능을 확인하고 최적의 LSTM 모델을 구성했다. 같은 유형은 새로운 데이터에 대해서도 잘 동작하는 것을 보였다.

• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.166-171
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• 2023

The Stock price analysis is an increasing concern in a financial time series. The purpose of the study is to analyze the price parameters of date, high, low, and news feed about the stock exchange price. Long short term memory (LSTM) is a cutting-edge technology used for predicting the data based on time series. LSTM performs well in executing large sequence of data. This paper presents the Long Short Term Memory Model has used to analyze the stock price ranges of 10 days and 20 days by exponential moving average. The proposed approach gives better performance using technical indicators of stock price with an accuracy of 82.6% and cross entropy of 71%.