• Title/Summary/Keyword: time series regression

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A Study on the Comparison of Electricity Forecasting Models: Korea and China

  • Zheng, Xueyan;Kim, Sahm
    • Communications for Statistical Applications and Methods
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    • v.22 no.6
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    • pp.675-683
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    • 2015
  • In the 21st century, we now face the serious problems of the enormous consumption of the energy resources. Depending on the power consumption increases, both China and South Korea face a reduction in available resources. This paper considers the regression models and time-series models to compare the performance of the forecasting accuracy based on Mean Absolute Percentage Error (MAPE) in order to forecast the electricity demand accurately on the short-term period (68 months) data in Northeast China and find the relationship with Korea. Among the models the support vector regression (SVR) model shows superior performance than time-series models for the short-term period data and the time-series models show similar results with the SVR model when we use long-term period data.

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 Quantitative Model for the Projection of Health Expenditure (의료비 결정요인 분석을 위한 계량적 모형 고안)

  • Kim, Han-Joong;Lee, Young-Doo;Nam, Chung-Mo
    • Journal of Preventive Medicine and Public Health
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    • v.24 no.1 s.33
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    • pp.29-36
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    • 1991
  • A multiple regression analysis using ordinary least square (OLS) is frequently used for the projection of health expenditure as well as for the identification of factors affecting health care costs. Data for the analysis often have mixed characteristics of time series and cross section. Parameters as a result of OLS estimation, in this case, are no longer the best linear unbiased estimators (BLUE) because the data do not satisfy basic assumptions of regression analysis. The study theoretically examined statistical problems induced when OLS estimation was applied with the time series cross section data. Then both the OLS regression and time series cross section regression (TSCS regression) were applied to the same empirical da. Finally, the difference in parameters between the two estimations were explained through residual analysis.

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Parameter Estimation and Prediction for NHPP Software Reliability Model and Time Series Regression in Software Failure Data

  • Song, Kwang-Yoon;Chang, In-Hong
    • Journal of Integrative Natural Science
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    • v.7 no.1
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    • pp.67-73
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    • 2014
  • We consider the mean value function for NHPP software reliability model and time series regression model in software failure data. We estimate parameters for the proposed models from two data sets. The values of SSE and MSE is presented from two data sets. We compare the predicted number of faults with the actual two data sets using the mean value function and regression curve.

Research Trends Analysis of Information Security using Text Mining (텍스트마이닝을 이용한 정보보호 연구동향 분석)

  • Kim, Taekyung;Kim, Changsik
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.14 no.2
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    • pp.19-25
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    • 2018
  • With the development of IT technology, various services such as artificial intelligence and autonomous vehicles are being introduced, and many changes are taking place in our lives. However, if secure security is not provided, it will cause many risks, so the information security becomes more important. In this paper, we analyzed the research trends of main themes of information security over time. In order to conduct the research, 'Information Security' was searched in the Web of Science database. Using the abstracts of theses published from 1991 to 2016, we derived main research topics through topic modeling and time series regression analysis. The topic modeling results showed that the research topics were Information technology, system access, attack, threat, risk management, network type, security management, security awareness, certification level, information protection organization, security policy, access control, personal information, security investment, computing environment, investment cost, system structure, authentication method, user behavior, encryption. The time series regression results indicated that all the topics were hot topics.

Some model misspecification problems for time series: A Monte Carlo investigation

  • Dong-Bin Jeong
    • Communications for Statistical Applications and Methods
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    • v.5 no.1
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    • pp.55-67
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    • 1998
  • Recent work by Shin and Sarkar (1996) examines model misspecification problems for nonstationary time series. Shin and Sarkar introduce a general regression model with integrated errors and one system of integrated regressors and discuss the limiting distributions of the OLS estimators and the usual OLS statistics such as $\hat{\sigma^2}$t, DW and $R^2$. We analyze three different model misspecification problems through a Monte Carlo study and investigate each model misspecification problem. Our Monte Carlo experiments show that DW and $R^2$ can be in general used as diagnostic tools to detect spurious regression, misspecification of nonstationary autoregressive and polynomial regression models.

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Forecasting hierarchical time series for foodborne disease outbreaks (식중독 발생 건수에 대한 계층 시계열 예측)

  • In-Kwon Yeo
    • The Korean Journal of Applied Statistics
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    • v.37 no.4
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    • pp.499 -508
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    • 2024
  • In this paper, we investigate hierarchical time series forecasting that adhere to a hierarchical structure when deriving predicted values by analyzing segmented data as well as aggregated datasets. The occurrences of food poisoning by a specific pathogen are analyzed using zero-inflated Poisson regression models and negative binomial regression models. The occurrences of major, miscellaneous, and overall food poisoning are analyzed using Poisson regression models and negative binomial regression models. For hierarchical time series forecasting, the MinT estimation proposed by Wickramasuriya et al. (2019) is employed. Negative predicted values resulting from hierarchical adjustments are adjusted to zero, and weights are multiplied to the remaining lowest-level variables to satisfy the hierarchical structure. Empirical analysis revealed that there is little difference between hierarchical and non-hierarchical adjustments in predictions based on pathogens. However, hierarchical adjustments generally yield superior results for predictions concerning major, miscellaneous, and overall occurrences. Without hierarchical adjustment, instances may occur where the predicted frequencies of the lowest-level variables exceed that of major or miscellaneous occurrences. However, the proposed method enables the acquisition of predictions that adhere to the hierarchical structure.

Application of Statistical Models for Default Probability of Loans in Mortgage Companies

  • Jung, Jin-Whan
    • Communications for Statistical Applications and Methods
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    • v.7 no.2
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    • pp.605-616
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    • 2000
  • Three primary interests frequently raised by mortgage companies are introduced and the corresponding statistical approaches for the default probability in mortgage companies are examined. Statistical models considered in this paper are time series, logistic regression, decision tree, neural network, and discrete time models. Usage of the models is illustrated using an artificially modified data set and the corresponding models are evaluated in appropriate manners.

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Common Feature Analysis of Economic Time Series: An Overview and Recent Developments

  • Centoni, Marco;Cubadda, Gianluca
    • Communications for Statistical Applications and Methods
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    • v.22 no.5
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    • pp.415-434
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    • 2015
  • In this paper we overview the literature on common features analysis of economic time series. Starting from the seminal contributions by Engle and Kozicki (1993) and Vahid and Engle (1993), we present and discuss the various notions that have been proposed to detect and model common cyclical features in macroeconometrics. In particular, we analyze in details the link between common cyclical features and the reduced-rank regression model. We also illustrate similarities and differences between the common features methodology and other popular types of multivariate time series modelling. Finally, we discuss some recent developments in this area, such as the implications of common features for univariate time series models and the analysis of common autocorrelation in medium-large dimensional systems.

Model Checking for Time-Series Count Data

  • Lee, Sung-Im
    • Communications for Statistical Applications and Methods
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    • v.12 no.2
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    • pp.359-364
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    • 2005
  • This paper considers a specification test of conditional Poisson regression model for time series count data. Although conditional models for count data have received attention and proposed in several ways, few studies focused on checking its adequacy. Motivated by the test of martingale difference assumption, a specification test via Ljung-Box statistic is proposed in the conditional model of the time series count data. In order to illustrate the performance of Ljung- Box test, simulation results will be provided.