• Title/Summary/Keyword: Predictive Variables

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Extended Forecasts of a Stock Index using Learning Techniques : A Study of Predictive Granularity and Input Diversity

  • Kim, Steven H.;Lee, Dong-Yun
    • Asia pacific journal of information systems
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    • v.7 no.1
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    • pp.67-83
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    • 1997
  • The utility of learning techniques in investment analysis has been demonstrated in many areas, ranging from forecasting individual stocks to entire market indexes. To date, however, the application of artificial intelligence to financial forecasting has focused largely on short predictive horizons. Usually the forecast window is a single period ahead; if the input data involve daily observations, the forecast is for one day ahead; if monthly observations, then a month ahead; and so on. Thus far little work has been conducted on the efficacy of long-term prediction involving multiperiod forecasting. This paper examines the impact of alternative procedures for extended prediction using knowledge discovery techniques. One dimension in the study involves temporal granularity: a single jump from the present period to the end of the forecast window versus a web of short-term forecasts involving a sequence of single-period predictions. Another parameter relates to the numerosity of input variables: a technical approach involving only lagged observations of the target variable versus a fundamental approach involving multiple variables. The dual possibilities along each of the granularity and numerosity dimensions entail a total of 4 models. These models are first evaluated using neural networks, then compared against a multi-input jump model using case based reasoning. The computational models are examined in the context of forecasting the S&P 500 index.

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A New Architecture of Genetically Optimized Self-Organizing Fuzzy Polynomial Neural Networks by Means of Information Granulation

  • Park, Ho-Sung;Oh, Sung-Kwun;Ahn, Tae-Chon
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.1505-1509
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    • 2005
  • This paper introduces a new architecture of genetically optimized self-organizing fuzzy polynomial neural networks by means of information granulation. The conventional SOFPNNs developed so far are based on mechanisms of self-organization and evolutionary optimization. The augmented genetically optimized SOFPNN using Information Granulation (namely IG_gSOFPNN) results in a structurally and parametrically optimized model and comes with a higher level of flexibility in comparison to the one we encounter in the conventional FPNN. With the aid of the information granulation, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. The GA-based design procedure being applied at each layer of genetically optimized self-organizing fuzzy polynomial neural networks leads to the selection of preferred nodes with specific local characteristics (such as the number of input variables, the order of the polynomial, a collection of the specific subset of input variables, and the number of membership function) available within the network. To evaluate the performance of the IG_gSOFPNN, the model is experimented with using gas furnace process data. A comparative analysis shows that the proposed IG_gSOFPNN is model with higher accuracy as well as more superb predictive capability than intelligent models presented previously.

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Maternal Role Development in Neonatal Intensive Care Unit Graduate Mothers of Premature Infant (신생아 집중 치료실 퇴원 후 미숙아 영아 어머니의 모성 역할 발달)

  • Kim, Ah Rim;Tak, Young Ran
    • Women's Health Nursing
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    • v.21 no.4
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    • pp.308-320
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    • 2015
  • Purpose: The aim of this study was to determine the predictive factors for maternal role development for mothers of premature infants. Methods: A descriptive correlational study was conducted. A total sample of 121 mothers of premature infants following discharge from the neonatal intensive care unit were recruited using two strategies; an internet-based survey and an in-person data collection in a tertiary university hospital in Korea. A self-report questionnaire was used to collect data regarding personal, birth variables, marital intimacy, maternal attachment, maternal identity and maternal role development. Results: A hierarchical multiple regression analysis indicated that parity, maternal attachment, marital intimacy and maternal identity were predictors for maternal role development for mothers of premature infants, accounting for 70% of the variance. Among these variables, maternal attachment is the most powerful predictor for maternal role development. Conclusion: Nursing interventions during hospitalization to post-discharge education that includes parents of premature babies with positive interaction between couples strengthening marital intimacy and promotes maternal attachment that leads to integrate maternal identity should be considered by priority. Community-based family services such as home visits should be focused on maximizing the predictive factors for maternal role development in transition to motherhood that can contribute to maternal health as well as optimal growth and development of premature infants.

Methodology for Determining Functional Forms in Developing Statistical Collision Models (교통사고모형 개발에서의 함수식 도출 방법론에 관한 연구)

  • Baek, Jong-Dae;Hummer, Joseph
    • International Journal of Highway Engineering
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    • v.14 no.5
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    • pp.189-199
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    • 2012
  • PURPOSES: The purpose of this study is to propose a new methodology for developing statistical collision models and to show the validation results of the methodology. METHODS: A new modeling method of introducing variables into the model one by one in a multiplicative form is suggested. A method for choosing explanatory variables to be introduced into the model is explained. A method for determining functional forms for each explanatory variable is introduced as well as a parameter estimating procedure. A model selection method is also dealt with. Finally, the validation results is provided to demonstrate the efficacy of the final models developed using the method suggested in this study. RESULTS: According to the results of the validation for the total and injury collisions, the predictive powers of the models developed using the method suggested in this study were better than those of generalized linear models for the same data. CONCLUSIONS: Using the methodology suggested in this study, we could develop better statistical collision models having better predictive powers. This was because the methodology enabled us to find the relationships between dependant variable and each explanatory variable individually and to find the functional forms for the relationships which can be more likely non-linear.

A Predictive Model of Quality of Life for Stomach Cancer Patients with Gastrectomy (위암수술 환자의 삶의 질 예측모형 구축)

  • Kim, Young Suk;Tae, Young Sook
    • Korean Journal of Adult Nursing
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    • v.27 no.6
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    • pp.613-623
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    • 2015
  • Purpose: This study was designed to construct a predictive model to explain quality of life of stomach cancer patients with gastrectomy. Methods: Data were collected from July 10 to August 30, 2013 through survey using self-reported questionnaires. A total of 218 patients with gastrectomy was recruited from three different hospitals. Outcome variables were exogenous ones (self efficacy and social support) and endogenous ones (depression, perceived health status, self care behavior, and quality of life). Results: Goodness-of-fit of the hypothetical model was $x^2=143.37$, RMSEA=.07 CFI=.95, TLI=.93 SRMR=.05. Self care behavior, depression and perceived health status had significant direct effects on quality of life. Self efficacy and social support were affected quality of life indirectly. These variables explained 67.9% of total variance of quality of life, and self-care behavior was the most influential factor for quality of life. Conclusion: The findings of this study suggested that self care behavior must be considered as an intervention strategy to improve quality of life. Also a development of a specific intervention program to promote self efficacy and control depression for patients with gastrectomy is essential to facilitate their self care behaviors.

Mapping Biodiversity throughoptimized selection of input variables in decision tree models (의사결정나무 변수 선정 방법을 적용한 대축적 생물다양성 지도 구축)

  • Kim, Do Yeon;Heo, Joon;Kim, Chang Jae
    • Journal of Environmental Impact Assessment
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    • v.20 no.5
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    • pp.663-673
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    • 2011
  • In the face of accelerating biodiversity loss and its significance in our coexistence with nature, biodiversity is becoming more crucial in sustainable development perspective. To estimate biodiversity in the future which provides valuable information for decision making system especially in the national level, a quantitative approach must be studied forehand as a baseline of the present status. In this study, we developed a large-scale map of Plant Species Richness (PSR, typical indicator of biodiversity) for Young-dong and Pyung-chang provinces. Due to the accessibility of appropriate data and advance of modelling techniques, reduction of variables without deteriorating the predictive power is considered by applying Genetic algorithm. In addition, a number of Correctly Classified Instances (CCI) with 10-fold cross validation which indicates the predictive power, was carried out for evaluation. This study, as a fundamental baseline, will be beneficial in future land work as well as ecosystem restoration business or other relevant decision making agenda.

Development of Regression Models Resolving High-Dimensional Data and Multicollinearity Problem for Heavy Rain Damage Data (호우피해자료에서의 고차원 자료 및 다중공선성 문제를 해소한 회귀모형 개발)

  • Kim, Jeonghwan;Park, Jihyun;Choi, Changhyun;Kim, Hung Soo
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.38 no.6
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    • pp.801-808
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    • 2018
  • The learning of the linear regression model is stable on the assumption that the sample size is sufficiently larger than the number of explanatory variables and there is no serious multicollinearity between explanatory variables. In this study, we investigated the difficulty of model learning when the assumption was violated by analyzing a real heavy rain damage data and we proposed to use a principal component regression model or a ridge regression model after integrating data to overcome the difficulty. We evaluated the predictive performance of the proposed models by using the test data independent from the training data, and confirmed that the proposed methods showed better predictive performances than the linear regression model.

Comparison of Predictive Value of Obesity and Lipid Related Variables for Metabolic Syndrome and Insulin Resistance in Obese Adults

  • Shin, Kyung A
    • Biomedical Science Letters
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    • v.25 no.3
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    • pp.256-266
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    • 2019
  • In this study, obese adults were compared for their ability to predict obesity and lipid related variables and their optimal cutoff values to predict metabolic syndrome and insulin resistance. In this study, 9,256 adults aged 20 years or older and less than 80 years old, who were in the Gyeonggi region from January 2014 to December 2016 and who were examined at a general hospital, were enrolled. The diagnostic criteria for obesity were WHO (World Health Organization), and BMI $25kg/m^2$ or more presented in the Asia-Pacific region. Metabolic syndrome was diagnosed based on the criteria of American Heart Association / National Heart, Lung, and Blood Institute (AHA / NHLBI). According to the results of receiver operating characteristic curve (ROC) analysis, Triglyceride / HDL-cholesterol (TG / HDL-C), Triglyceride and Glucose (TyG) index, lipid accumulation product (LAP) and visceral adiposity index (VAI) showed high predictive power for diagnosing metabolic syndrome. The diagnostic accuracy of LAP (AUC: 0.854) for males and VAI (0.888) for females was the highest. The optimal cutoff value of LAP was 42.71 for male and 35.44 for female, and the cutoff value of VAI was 1.92 for male and 2.15 for female. In addition, WHtR (waist to height ratio), TyG index, and LAP were used as predictors of insulin resistance in obese adults. Therefore, LAP and VAI were superior to other indicators in predicting metabolic syndrome in obese adults.

Comparison of time series predictions for maximum electric power demand (최대 전력수요 예측을 위한 시계열모형 비교)

  • Kwon, Sukhui;Kim, Jaehoon;Sohn, SeokMan;Lee, SungDuck
    • The Korean Journal of Applied Statistics
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    • v.34 no.4
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    • pp.623-632
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    • 2021
  • Through this study, we studied how to consider environment variables (such as temperatures, weekend, holiday) closely related to electricity demand, and how to consider the characteristics of Korea electricity demand. In order to conduct this study, Smoothing method, Seasonal ARIMA model and regression model with AR-GARCH errors are compared with mean absolute error criteria. The performance comparison results of the model showed that the predictive method using AR-GARCH error regression model with environment variables had the best predictive power.

An Empirical Study on the Correlation Between Marrical Communication Types and Demographic Socialogical Variables -on Some of the married Seoulites- (부부간 의사소통유형과 자존감-서울시를 중심으로-)

  • 서수경
    • Journal of the Korean Home Economics Association
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    • v.29 no.2
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    • pp.199-215
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    • 1991
  • The purpose of the study is to investigate the relation between marital communication types and self-esteem demographic-socioligical variables to eliminate the methodlogical contradiction of previous studies, this study aimed as follows: (1) correlation between marital communication types and all the independent variables, such as self-esteem, sex, age, academic history, mate selection type, family type, duration of marriage, religion, etc. and marital communication types. (2) discriminating powers of marital communication types by way of the variables mentioned above. In this study questionaire was used on 392 married Seoulites, which consists of two scales and 9 items. In order to verify the hypotheses, the following two methods were used: (1) LOGIT program to the correlation between norminal scale and /or interval scale. (2) discriminating analysis of marital communication types by way of the variables. The results of the study are as follows: (1) Only 4 variables, such as self-esteem, sex, duration of marriage and academic history, correlate with marital communication types in level P<0.05 (2) According to the discriminating analysis of the variables mentioned above, marital communication types cannot be predicted as the predictive power is only 32.2%. (3) Correlation coefficent of authoritic communication type is higher males than females and higher low degree group of self-esteem than high degree group. (4) In the some communication types, direction of correlation coefficent is different in the some variables.

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