• Title/Summary/Keyword: Long-term Time Series

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시계열 자료에 나타나는 장기 기억 속성에 대한 추정 및 검정 :NYSE composite index에 대한 실증분석

  • 남재우;이회경
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 1998.10a
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    • pp.271-274
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    • 1998
  • In this paper we examine long-term memory of the financial time-series by employing the R/S analysis, the Hurst exponent estimation, and the modified R/S analysis. The null hypothesis of white-noise is tested using the NYSE daily indexes from January 1966 to July 1998, and the results show that long-range dependence exists before the apparent structural break of the Black Monday in 1987.

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Study on the Long-Term Demand Projections for Timber in Korea (우리나라 목재수요(木材需要)의 장기여측에(長期予測) 관(関)한 연구(硏究))

  • Kim, Jang Soo;Park, Ho Tak
    • Journal of Korean Society of Forest Science
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    • v.50 no.1
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    • pp.29-35
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    • 1980
  • The purpose of this study is to analyze and to forecast the long-term domestic demand and export demand for timber in Korea by regression models with time series data during 1962~1978. The method applied in this study was econometric analysis using Time Series Processor. The most important explanatory variables of timber demand were found to be the production activities of wood products industries to the prices of substitute goods. On the basis of the long-term forecast made according to the guidelines of the Fifth Five-Year Plan. According to the projection, domestic timber demand is projected at 8 million cubic meters in 1987 and 10.6 million cubic meters in 1991. On the other hand, the total demand (domestic demand plus export demand) for timber is projected 21.4 million cubic meters in 1987 and 27.2 million cubic meters in 1991.

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Experimental Study on Long-Term Prediction of Rebar Price Using Deep Learning Recursive Prediction Meothod (딥러닝의 반복적 예측방법을 활용한 철근 가격 장기예측에 관한 실험적 연구)

  • Lee, Yong-Seong;Kim, Kyung-Hwan
    • Korean Journal of Construction Engineering and Management
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    • v.22 no.3
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    • pp.21-30
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    • 2021
  • This study proposes a 5-month rebar price prediction method using the recursive prediction method of deep learning. This approach predicts a long-term point in time by repeating the process of predicting all the characteristics of the input data and adding them to the original data and predicting the next point in time. The predicted average accuracy of the rebar prices for one to five months is approximately 97.24% in the manner presented in this study. Through the proposed method, it is expected that more accurate cost planning will be possible than the existing method by supplementing the systematicity of the price estimation method through human experience and judgment. In addition, it is expected that the method presented in this study can be utilized in studies that predict long-term prices using time series data including building materials other than rebar.

A Novel Framework Based on CNN-LSTM Neural Network for Prediction of Missing Values in Electricity Consumption Time-Series Datasets

  • Hussain, Syed Nazir;Aziz, Azlan Abd;Hossen, Md. Jakir;Aziz, Nor Azlina Ab;Murthy, G. Ramana;Mustakim, Fajaruddin Bin
    • Journal of Information Processing Systems
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    • v.18 no.1
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    • pp.115-129
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    • 2022
  • Adopting Internet of Things (IoT)-based technologies in smart homes helps users analyze home appliances electricity consumption for better overall cost monitoring. The IoT application like smart home system (SHS) could suffer from large missing values gaps due to several factors such as security attacks, sensor faults, or connection errors. In this paper, a novel framework has been proposed to predict large gaps of missing values from the SHS home appliances electricity consumption time-series datasets. The framework follows a series of steps to detect, predict and reconstruct the input time-series datasets of missing values. A hybrid convolutional neural network-long short term memory (CNN-LSTM) neural network used to forecast large missing values gaps. A comparative experiment has been conducted to evaluate the performance of hybrid CNN-LSTM with its single variant CNN and LSTM in forecasting missing values. The experimental results indicate a performance superiority of the CNN-LSTM model over the single CNN and LSTM neural networks.

Optimal Inflation Threshold and Economic Growth: Ordinal Regression Model Analysis

  • DINH, Doan Van
    • The Journal of Asian Finance, Economics and Business
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    • v.7 no.5
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    • pp.91-102
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    • 2020
  • The study investigates the relationship between the inflation rate and economic growth to find out the optimal inflation threshold for economic growth. Therefore, this study applied an ordinary least square model (OLS) and the ordinal regression model, and collected the time-series data from 1996 to 2017 to test the relationship between inflation and economic growth in the short-term and long-term. The sample fits the model and is statistically significant. The study showed that 96.6% of correlation between inflation rate and economic growth are close and 4.5% of optimal inflation threshold is appropriate for economic growth. It finds that the optimal inflation threshold is base to perform economic growth, besides the inflation rate is positively related to economic growth. The results support the monetary policy appropriately. This study identifies issues for Government to consider: have a comprehensive solution among macroeconomic policies, monetary policy, fiscal policy and other policies to control and maintain the inflation and stimulate growth; have appropriate policies to regulate inflation to stimulate economic growth over the long term; set a priority goal for sustainable economic growth; not pursue economic growth by maintaining the inflation rate in the long term, but take appropriate measures to stabilize the inflation at the optimal inflation threshold.

Prediction of long-term wind speed and capacity factor using Measure-Correlate-Predict method (측정-상관-예측법을 이용한 장기간 풍속 및 설비이용률의 예측)

  • Ko, Kyung-Nam;Huh, Jong-Chul
    • Journal of the Korean Solar Energy Society
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    • v.32 no.6
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    • pp.37-43
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    • 2012
  • Long-term variations in wind speed and capacity factor(CF) on Seongsan wind farm of Jeju Island, South Korea were derived statistically. The selected areas for this study were Subji, having a year wind data at 30m above ground level, Sinsan, having 30-year wind data at 10m above ground level and Seongsan wind farm, where long-term CF was predicted. The Measure-Correlate-Predict module of WindPRO was used to predict long-tem wind characteristics at Seongsan wind farm. Eachyear's CF was derived from the estimated 30-year time series wind data by running WAsP module. As a result, for the 30-year CFs, Seongsan wind farm was estimated to have 8.3% for the coefficien to fvariation, CV, and-16.5% ~ 13.2% for the range of variation, RV. It was predicted that the annual CF at Seongsan wind farm varied within about ${\pm}4%$.

Long-Term Water Quality Trend Analysis with NTrend 1.0 Program in Nakdong River (NTrend 1.0에 의한 낙동강 수질 장기변동 추세분석)

  • Yu, Jae Jeong;Shin, Suk Ho;Yoon, Young Sam;Song, Jae Kee
    • Journal of Korean Society on Water Environment
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    • v.26 no.6
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    • pp.895-902
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    • 2010
  • The effect of seasonality on water quality variation is very significant. Generally, it reduce the power of the trend extraction. A parametric time-series model was used for detecting trends in historic constituent concentration data. The effect of seasonality is able to remove from time series decomposition technique. According to such statistic methode, long-term water quality trend analysis system (NTrend 1.0) was developed by Nakdong River Water Environmental Research Center. The trend analysis of BOD variation was conducted with NTrend 1.0 at Goreong and Moolkum site in Nakdong river to show the effect of water quality management action plan. Power test of trend extraction was tried each case of 'deseasonalized and deannulized' data and 'deseasonalized' data. Analysis period was from 1989 to 2006, and it's period was divided again three times, 1989~1993, 1994~1999 and 2000~2006 according to action plan period. The BOD trend was downward in Goreong site during three times and it's trend slope was very steep, and upward in Moolkum during 1989~1993, but it was turned downward during 1994~1999 and 2000~2006. It was revealed that it's very effective to reduce the concentration of BOD by water quality management action plan in that watershed. The result of power test was shown that it is high for trend extraction power in case of 'deseasonalized' data.

Trends in the utilization of dental outpatient services affected by the expansion of health care benefits in South Korea to include scaling: a 6-year interrupted time-series study

  • Park, Hee-Jung;Lee, Jun Hyup;Park, Sujin;Kim, Tae-Il
    • Journal of Periodontal and Implant Science
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    • v.48 no.1
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    • pp.3-11
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    • 2018
  • Purpose: This study utilized a strong quasi-experimental design to test the hypothesis that the implementation of a policy to expand dental care services resulted in an increase in the usage of dental outpatient services. Methods: A total of 45,650,000 subjects with diagnoses of gingivitis or advanced periodontitis who received dental scaling were selected and examined, utilizing National Health Insurance claims data from July 2010 through November 2015. We performed a segmented regression analysis of the interrupted time-series to analyze the time-series trend in dental costs before and after the policy implementation, and assessed immediate changes in dental costs. Results: After the policy change was implemented, a statistically significant 18% increase occurred in the observed total dental cost per patient, after adjustment for age, sex, and residence area. In addition, the dental costs of outpatient gingivitis treatment increased immediately by almost 47%, compared with a 15% increase in treatment costs for advanced periodontitis outpatients. This policy effect appears to be sustainable. Conclusions: The introduction of the new policy positively impacted the immediate and long-term outpatient utilization of dental scaling treatment in South Korea. While the policy was intended to entice patients to prevent periodontal disease, thus benefiting the insurance system, our results showed that the policy also increased treatment accessibility for potential periodontal disease patients and may improve long-term periodontal health in the South Korean population.

Time dependent finite element analysis of steel-concrete composite beams considering partial interaction

  • Dias, Maiga M.;Tamayo, Jorge L.P.;Morsch, Inacio B.;Awruch, Armando M.
    • Computers and Concrete
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    • v.15 no.4
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    • pp.687-707
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    • 2015
  • A finite element computer code for short-term analysis of steel-concrete composite structures is extended to study long-term effects under service loads, in the present work. Long-term effects are important in engineering design because they influence stress and strain distribution of the structural system and therefore contribute to the increment of deflections in these structures. For creep analysis, a rheological model based on a Kelvin chain, with elements placed in series, was employed. The parameters of the Kelvin chain were obtained using Dirichlet series. Creep and shrinkage models, proposed by the CEB FIP 90, were used. The shear-lag phenomenon that takes place at the concrete slab is usually neglected or not properly taken into account in the formulation of beam-column finite elements. Therefore, in this work, a three-dimensional numerical model based on the assemblage of shell finite elements for representing the steel beam and the concrete slab is used. Stud shear connectors are represented for special beam-column elements to simulate the partial interaction at the slab-beam interface. The two-dimensional representation of the concrete slab permits to capture the non-uniform shear stress distribution in the horizontal plane of the slab due to shear-lag phenomenon. The model is validated with experimental results of two full-scale continuous composite beams previously studied by other authors. Results are given in terms of displacements, bending moments and cracking patterns in order to shown the influence of long-term effects in the structural response and also the potentiality of the present numerical code.

Investigation of Korean Precipitation Variability using EOFs and Cyclostationary EOFs (EOF와 CSEOF를 이용한 한반도 강수의 변동성 분석)

  • Kim, Gwang-Seob;Sun, Ming-Dong
    • Proceedings of the Korea Water Resources Association Conference
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    • 2009.05a
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    • pp.1260-1264
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    • 2009
  • Precipitation time series is a mixture of complicate fluctuation and changes. The monthly precipitation data of 61 stations during 36 years (1973-2008) in Korea are comprehensively analyzed using the EOFs technique and CSEOFs technique respectively. The main motivation for employing this technique in the present study is to investigate the physical processes associated with the evolution of the precipitation from observation data. The twenty-five leading EOF modes account for 98.05% of the total monthly variance, and the first two modes account for 83.68% of total variation. The first mode exhibits traditional spatial pattern with annual cycle of corresponding PC time series and second mode shows strong North South gradient. In CSEOF analysis, the twenty-five leading CSEOF modes account for 98.58% of the total monthly variance, and the first two modes account for 78.69% of total variation, these first two patterns' spatial distribution show monthly spatial variation. The corresponding mode's PC time series reveals the annual cycle on a monthly time scale and long-term fluctuation and first mode's PC time series shows increasing linear trend which represents that spatial and temporal variability of first mode pattern has strengthened. Compared with the EOFs analysis, the CSEOFs analysis preferably exhibits the spatial distribution and temporal evolution characteristics and variability of Korean historical precipitation.

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