• Title, Summary, Keyword: prediction model

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A Climate Prediction Method Based on EMD and Ensemble Prediction Technique

  • Bi, Shuoben;Bi, Shengjie;Chen, Xuan;Ji, Han;Lu, Ying
    • Asia-Pacific Journal of Atmospheric Sciences
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    • v.54 no.4
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    • pp.611-622
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    • 2018
  • Observed climate data are processed under the assumption that their time series are stationary, as in multi-step temperature and precipitation prediction, which usually leads to low prediction accuracy. If a climate system model is based on a single prediction model, the prediction results contain significant uncertainty. In order to overcome this drawback, this study uses a method that integrates ensemble prediction and a stepwise regression model based on a mean-valued generation function. In addition, it utilizes empirical mode decomposition (EMD), which is a new method of handling time series. First, a non-stationary time series is decomposed into a series of intrinsic mode functions (IMFs), which are stationary and multi-scale. Then, a different prediction model is constructed for each component of the IMF using numerical ensemble prediction combined with stepwise regression analysis. Finally, the results are fit to a linear regression model, and a short-term climate prediction system is established using the Visual Studio development platform. The model is validated using temperature data from February 1957 to 2005 from 88 weather stations in Guangxi, China. The results show that compared to single-model prediction methods, the EMD and ensemble prediction model is more effective for forecasting climate change and abrupt climate shifts when using historical data for multi-step prediction.

A Study on the Predictability of Hospital's Future Cash Flow Information (병원의 미래 현금흐름 정보예측)

  • Moon, Young-Jeon;Yang, Dong-Hyun
    • Korea Journal of Hospital Management
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    • v.11 no.3
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    • pp.19-41
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    • 2006
  • The Objective of this study was to design the model which predict the future cash flow of hospitals and on the basis of designed model to support sound hospital management by the prediction of future cash flow. The five cash flow measurement variables discussed in financial accrual part were used as variables and these variables were defined as NI, NIDPR, CFO, CFAI, CC. To measure the cash flow B/S related variables, P/L related variables and financial ratio related variables were utilized in this study. To measure cash flow models were designed and to estimate the prediction ability of five cash flow models, the martingale model and the market model were utilized. To estimate relative prediction outcome of cash flow prediction model and simple market model, MAE and MER were used to compare and analyze relative prediction ability of the cash flow model and the market model and to prove superiority of the model of the cash flow prediction model, 32 Regional Public Hospital's cross-section data and 4 year time series data were combined and pooled cross-sectional time series regression model was used for GLS-analysis. To analyze this data, Firstly, each cash flow prediction model, martingale model and market model were made and MAE and MER were estimated. Secondly difference-test was conducted to find the difference between MAE and MER of cash flow prediction model. Thirdly after ranking by size the prediction of cash flow model, martingale model and market model, Friedman-test was evaluated to find prediction ability. The results of this study were as follows: when t-test was conducted to find prediction ability among each model, the error of prediction of cash flow model was smaller than that of martingale and market model, and the difference of prediction error cash flow was significant, so cash flow model was analyzed as excellent compare with other models. This research results can be considered conductive in that present the suitable prediction model of future cash flow to the hospital. This research can provide valuable information in policy-making of hospital's policy decision. This research provide effects as follows; (1) the research is useful to estimate the benefit of hospital, solvency and capital supply ability for substitution of fixed equipment. (2) the research is useful to estimate hospital's liqudity, solvency and financial ability. (3) the research is useful to estimate evaluation ability in hospital management. Furthermore, the research should be continued by sampling all hospitals and constructed advanced cash flow model in dimension, established type and continued by studying unified model which is related each cash flow model.

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Dam Sensor Outlier Detection using Mixed Prediction Model and Supervised Learning

  • Park, Chang-Mok
    • International journal of advanced smart convergence
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    • v.7 no.1
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    • pp.24-32
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    • 2018
  • An outlier detection method using mixed prediction model has been described in this paper. The mixed prediction model consists of time-series model and regression model. The parameter estimation of the prediction model was performed using supervised learning and a genetic algorithm is adopted for a learning method. The experiments were performed in artificial and real data set. The prediction performance is compared with the existing prediction methods using artificial data. Outlier detection is conducted using the real sensor measurements in a dam. The validity of the proposed method was shown in the experiments.

A Novel Data Prediction Model using Data Weights and Neural Network based on R for Meaning Analysis between Data (데이터간 의미 분석을 위한 R기반의 데이터 가중치 및 신경망기반의 데이터 예측 모형에 관한 연구)

  • Jung, Se Hoon;Kim, Jong Chan;Sim, Chun Bo
    • Journal of Korea Multimedia Society
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    • v.18 no.4
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    • pp.524-532
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    • 2015
  • All data created in BigData times is included potentially meaning and correlation in data. A variety of data during a day in all society sectors has become created and stored. Research areas in analysis and grasp meaning between data is proceeding briskly. Especially, accuracy of meaning prediction and data imbalance problem between data for analysis is part in course of something important in data analysis field. In this paper, we proposed data prediction model based on data weights and neural network using R for meaning analysis between data. Proposed data prediction model is composed of classification model and analysis model. Classification model is working as weights application of normal distribution and optimum independent variable selection of multiple regression analysis. Analysis model role is increased prediction accuracy of output variable through neural network. Performance evaluation result, we were confirmed superiority of prediction model so that performance of result prediction through primitive data was measured 87.475% by proposed data prediction model.

A Study for Examination of Road Noise Prediction Results According to 3-d Noise Prediction Models and Input Parameters (3차원 소음예측모델 및 입력변수 변화에 따른 도로소음 예측결과 검토에 대한 연구)

  • Sun, Hyosung
    • Journal of Environmental Impact Assessment
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    • v.23 no.2
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    • pp.112-118
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    • 2014
  • The application of a 3-d noise prediction model is increasing as a tool for performing actual noise assessment in order to investigate the noise impact of the residential facility around a development region. However, because the appropriate plans of applying a 3-d noise prediction model is insufficient, it is important to secure the reliability of the noise prediction results generated by a 3-d noise prediction model. Therefore, this study is focused on examining a 3-d noise prediction model, and a prediction equation and input data in it. For this, the 3-d noise prediction models such as SoundPLAN, Cadna-A, IMMI is applied in road noise. After the contents of road noise equations, input data of road noise source, and input data of road noise barrier are understood, the road noise prediction results are compared and examined according to the variation of 3-d noise prediction model, road noise equation, and input data of road noise source and road noise barrier.

Developing an User Location Prediction Model for Ubiquitous Computing based on a Spatial Information Management Technique

  • Choi, Jin-Won;Lee, Yung-Il
    • Architectural research
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    • v.12 no.2
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    • pp.15-22
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    • 2010
  • Our prediction model is based on the development of "Semantic Location Model." It embodies geometrical and topological information which can increase the efficiency in prediction and make it easy to manipulate the prediction model. Data mining is being implemented to extract the inhabitant's location patterns generated day by day. As a result, the self-learning system will be able to semantically predict the inhabitant's location in advance. This context-aware system brings about the key component of the ubiquitous computing environment. First, we explain the semantic location model and data mining methods. Then the location prediction model for the ubiquitous computing system is described in details. Finally, the prototype system is introduced to demonstrate and evaluate our prediction model.

Development of a Multipurpose-Oriented Environmental Prediction Model for Plant Production System - Construction of the Basic System and its Application - (식물생산시스템의 다목적 환경예측 모델의 개발 -기본 시스템 구축 및 응용-)

  • 손정익;이동근;김문기
    • Protected Horticulture and Plant Factory
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    • v.2 no.2
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    • pp.126-135
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    • 1993
  • Recently, the characteristic of plant production systems in Korea has been changed with the strong trends of integration and large scale, using environmental control techniques. To satisfy this change successfully, first of all, the environmental prediction inside the system must be preceded. While many environmental prediction models for plant production system were developed by many persons, each model cannot be applied to the every situation without the perfect understanding of source codes and the technical modification. The purpose of this study is building the environmental prediction model to predict and evaluate the environment inside the system numerically, and also developing the multipurpose program available for practical design. The model consisted of the basic system model, the cultivation related model and the environmental control related model. The contents of each model are as follows : the basic system model is dealing with thermal and light environments, soil environment and ventilation : the cultivation related model with soil and hydroponic cultures ; and the environmental control related model with thermal curtain and heat exchanging system. The environmental prediction model was developed using a common simulation program, PCSMP, so that it could be easily understood and modified by anyone. Finally, the model was executed and verified through comparison between simulated and measured results for soil culture, and both results showed good agreements.

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A methodology for creating a function-centered reliability prediction model (기능 중심의 신뢰성 예측 모델링 방법론)

  • Chung, Yong-ho;Park, Ji-Myoung;Jang, Joong-Soon;Park, Sang-Chul
    • Journal of the Korea Society for Simulation
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    • v.25 no.4
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    • pp.77-84
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    • 2016
  • This paper proposes a methodology for creating a function based reliability prediction model. Although, there are various works for reliability prediction, one of the features of their research is that the research is based on hardware-centered reliability prediction. Reliability is often defined as the probability that a device will perform its intended function, under operating condition, for a specified period of time, there is a profound irony about reliability prediction problem. In this paper, we proposed four-phase modeling procedure for function-centered reliability prediction. The proposed modeling procedure consists of four models; 1) structure block model, 2) function block model, 3) device model, and 4) reliability prediction model. We performed function-centered reliability prediction for electronic ballast using the proposed modeling procedure and MIL-HDBK-217F which is the military handbook for reliability prediction of electronic equipment.

Early Software Quality Prediction Using Support Vector Machine (Support Vector Machine을 이용한 초기 소프트웨어 품질 예측)

  • Hong, Euy-Seok
    • Journal of Information Technology Services
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    • v.10 no.2
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    • pp.235-245
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    • 2011
  • Early criticality prediction models that determine whether a design entity is fault-prone or not are becoming more and more important as software development projects are getting larger. Effective predictions can reduce the system development cost and improve software quality by identifying trouble-spots at early phases and proper allocation of effort and resources. Many prediction models have been proposed using statistical and machine learning methods. This paper builds a prediction model using Support Vector Machine(SVM) which is one of the most popular modern classification methods and compares its prediction performance with a well-known prediction model, BackPropagation neural network Model(BPM). SVM is known to generalize well even in high dimensional spaces under small training data conditions. In prediction performance evaluation experiments, dimensionality reduction techniques for data set are not used because the dimension of input data is too small. Experimental results show that the prediction performance of SVM model is slightly better than that of BPM and polynomial kernel function achieves better performance than other SVM kernel functions.

Development of technique for slope hazards prediction using decision tree model (의사결정나무모형을 이용한 급경사지재해 예측기법 개발)

  • Song, Young-Suk;Cho, Yong-Chan;Chae, Byung-Gon
    • Proceedings of the Korean Geotechical Society Conference
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    • pp.233-242
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    • 2009
  • Based on the data obtained from field investigation and soil testing to slope hazards occurrence section and non-occurrence section in crystalline rocks like gneiss, granite, and so on, a prediction model was developed by the use of a decision tree model. The classification standard of the selected prediction model is composed of the slope angle, the coefficient of permeability and the void ratio in the order. The computer program, SHAPP ver. 1.0 for prediction of slope hazards around an important national facilities using GIS technique and the developed model. To prove the developed prediction model and the computer program, the field data surveyed from Jumunjin, Gangneung city were compared with the prediction result in the same site. As the result of comparison, the real occurrence location of slope hazards was similar to the predicted section. Through the continuous study, the accuracy about prediction result of slope hazards will be upgraded and the computer program will be commonly used in practical.

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