• Title/Summary/Keyword: time series

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Development of the Roundwood Demand Prediction Model

  • Kim, Dong-Jun
    • Journal of Korean Society of Forest Science
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    • v.95 no.2
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    • pp.203-208
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    • 2006
  • This study compared the roundwood demand prediction accuracy of econometric and time-series models using Korean data. The roundwood was divided into softwood and hardwood by species. The econometric model of roundwood demand was specified with four explanatory variables; own price, substitute price, gross domestic product, dummy. The time-series model was specified with lagged endogenous variable. The dummy variable reflected the abrupt decrease in roundwood demand in the late 1990's in the case of softwood roundwood, and the boom of plywood export in the late 1970's in the case of hardwood roundwood. On the other hand, the prediction accuracy was estimated on the basis of Residual Mean Square Errors(RMSE). The results showed that the softwood roundwood demand prediction can be performed more accurately by econometric model than by time-series model. However, the hardwood roundwood demand prediction accuracy was similar in the case of using econometric and time-series model.

Comparison of Forecasting Performance in Multivariate Nonstationary Seasonal Time Series Models (다변량 비정상 계절형 시계열모형의 예측력 비교)

  • Seong, Byeong-Chan
    • Communications for Statistical Applications and Methods
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    • v.18 no.1
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    • pp.13-21
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    • 2011
  • This paper studies the analysis of multivariate nonstationary time series with seasonality. Three types of multivariate time series models are considered: seasonal cointegration model, nonseasonal cointegration model with seasonal dummies, and vector autoregressive model in seasonal differences that are compared for forecasting performances using Korean macro-economic time series data. The cointegration models produce smaller forecast errors in short horizons; however, when longer forecasting periods are considered the vector autoregressive model appears preferable.

Forecast of Influent Characteristics in Wastewater Treatment Plant with Time Series Model (시계열모델을 이용한 하수처리장 유입수 성상 예측)

  • Kim, Byung-Goon;Moon, Yong-Taik;Kim, Hong-Suck;Kim, Jong-Rack
    • Journal of Korean Society of Water and Wastewater
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    • v.21 no.6
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    • pp.701-707
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    • 2007
  • The information on the incoming load to wastewater treatment plants is not often available to apply to evaluate effects of control actions on the field plant. In this study, a time series model was developed to forecast influent flow rate, BOD, COD, SS, TN and TP concentrations using field operating data. The developed time series model could predict 1 day ahead forecasting results accurately. The coefficient of determination between measured data and 1 day ahead forecasting results has a range from 0.8898 to 0.9971. So, the corelation is relatively high. We made forecasting program based on the time series model developed and hope that the program will assist the operators in the stable operation in wastewater treatment plants.

Time Series Evaluation of Visual Fatigue and Depth Sensation Using a Stereoscopic Display

  • Kim, Sang-Hyun;Kishi, Shinsuke;Kawai, Takashi;Hatada, Toyohiko
    • Journal of Information Display
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    • v.10 no.4
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    • pp.188-194
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    • 2009
  • Conventional stereoscopic (3D) displays using binocular parallax generate unnatural conflicts between convergence and accommodation. These conflicts can affect the observer's ability to fuse binocular images and may cause visual fatigue. In this study, time series changes in visual fatigue and depth sensation when viewing stereoscopic images with changing parallax were examined. In particular, the physiological changes, including the subjective symptoms of visual fatigue, when viewing five parallax conditions, were examined. Then a comparative analysis of the 2D and 3D conditions was performed based on the visual function. To obtain data regarding the visual function, the time series changes in the spontaneous-blinking rate before and during the viewing of 3D images were measured. The time series change results suggest that 2D and 3D images cause significantly different types of visual fatigue over the range of binocular disparity.

Time-series InSAR Analysis and Post-processing Using ISCE-StaMPS Package for Measuring Bridge Displacements

  • Vadivel, Suresh Krishnan Palanisamy;Kim, Duk-jin;Kim, Young Cheol
    • Korean Journal of Remote Sensing
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    • v.36 no.4
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    • pp.527-534
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    • 2020
  • This study aims to monitor the displacement of the bridges using Stanford Method for Persistent Scatterers (StaMPS) time-series Persistent Scatterer Interferometric Synthetic Aperture Radar analysis. For case study bridges: Kimdaejung bridge and Deokyang bridge, we acquired 60 and 33 Cosmo-Skymed Synthetic Aperture Radar (SAR) data over the Mokpo region and Yeosu region, respectively from 2013 to 2019. With single-look interferograms, we estimated the long-term time-series displacements over the bridges. The time-series displacements were estimated as -8.8 mm/year and -1.34 mm/year at the mid-span over the selected bridges: Kimdaejung and Deokyang bridge, respectively. This time-series displacement provides reliable and high spatial resolution information to monitor the structural behavior of the bridge for preventing structural behaviors.

A Study on the Seasonal Adjustment of Time Series and Demand Forecasting for Electronic Product Sales (전자제품 판매매출액 시계열의 계절 조정과 수요예측에 관한 연구)

  • Seo, Myeong-Yul;Rhee, Jong-Tae
    • Journal of Applied Reliability
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    • v.3 no.1
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    • pp.13-40
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    • 2003
  • The seasonal adjustment is an essential process in analyzing the time series of economy and business. One of the powerful adjustment methods is X11-ARIMA Model which is popularly used in Korea. This method was delivered from Canada. However, this model has been developed to be appropriate for Canadian and American environment. Therefore, we need to review whether the X11-ARIMA Model could be used properly in Korea. In this study, we have applied the method to the annual sales of refrigerator sales in A electronic company. We appreciated the adjustment by result analyzing the time series components such as seasonal component, trend-cycle component, and irregular component, with the proposed method. Additionally, in order to improve the result of seasonal adjusted time series, we suggest the demand forecasting method base on autocorrelation and seasonality with the X11-ARIMA PROC.

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Effects of Overdispersion on Testing for Serial Dependence in the Time Series of Counts Data

  • Kim, Hee-Young;Park, You-Sung
    • Communications for Statistical Applications and Methods
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    • v.17 no.6
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    • pp.829-843
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    • 2010
  • To test for the serial dependence in time series of counts data, Jung and Tremayne (2003) evaluated the size and power of several tests under the class of INARMA models based on binomial thinning operations for Poisson marginal distributions. The overdispersion phenomenon(i.e., a variance greater than the expectation) is common in the real world. Overdispersed count data can be modeled by using alternative thinning operations such as random coefficient thinning, iterated thinning, and quasi-binomial thinning. Such thinning operations can lead to time series models of counts with negative binomial or generalized Poisson marginal distributions. This paper examines whether the test statistics used by Jung and Tremayne (2003) on serial dependence in time series of counts data are affected by overdispersion.

Effects of incorrect detrending on the coherency between non-stationary time series processes

  • Lee, Jin
    • Communications for Statistical Applications and Methods
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    • v.26 no.1
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    • pp.27-34
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    • 2019
  • We study the effect of detrending on the coherency between two time series processes. Many economic and financial time series variables include nonstationary components; however, we analyze the two most popular cases of stochastic and deterministic trends. We analyze the asymptotic behavior of coherency under incorrect detrending, which includes the cases of first-differencing the deterministic trend process and, conversely, the time trend removal of the unit root process. A simulation study is performed to investigate the finite sample performance of the sample coherency due to incorrect detrending. Our work is expected to draw attention to the possible distortion of coherency when the series are incorrectly detrended. Further, our results can extend to various specification of trends in aggregate time series variables.

Stochastic structures of world's death counts after World War II

  • Lee, Jae J.
    • Communications for Statistical Applications and Methods
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    • v.29 no.3
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    • pp.353-371
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    • 2022
  • This paper analyzes death counts after World War II of several countries to identify and to compare their stochastic structures. The stochastic structures that this paper entertains are three structural time series models, a local level with a random walk model, a fixed local linear trend model and a local linear trend model. The structural time series models assume that a time series can be formulated directly with the unobserved components such as trend, slope, seasonal, cycle and daily effect. Random effect of each unobserved component is characterized by its own stochastic structure and a distribution of its irregular component. The structural time series models use the Kalman filter to estimate unknown parameters of a stochastic model, to predict future data, and to do filtering data. This paper identifies the best-fitted stochastic model for three types of death counts (Female, Male and Total) of each country. Two diagnostic procedures are used to check the validity of fitted models. Three criteria, AIC, BIC and SSPE are used to select the best-fitted valid stochastic model for each type of death counts of each country.

Annual Precipitation Reconstruction Based on Tree-ring Data at Seorak (설악산 지역의 Tree-ring 자료를 이용한 연 강수량 재생성)

  • Kwak, Jae Won;Han, Heechan;Lee, Minjung;Kim, Hung Soo;Mun, Jangwon
    • Journal of Korean Society on Water Environment
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    • v.31 no.1
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    • pp.19-28
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    • 2015
  • The purpose of this research is reconstruction of annual precipitation based on Tree-ring series at Seorak mountain and examine its effectiveness. To do so we performed nonlinear time series characteristics test of Tree-ring series and reconstructed annual precipitation of Gangneung from 1687 to 1911 using Artificial neural network and Nonlinear autoregressive exogeneous input (NARX) model which reflects stochastic properties. As a result, Tree-ring series at Seorak Mountain shows nonlinear time series property and reconstructed annual precipitation series drawn from NARX is similar in statistical characteristics of observed annual time series.