• Title/Summary/Keyword: 시계열 그룹별 회귀모형

Search Result 2, Processing Time 0.015 seconds

Time series regression model for forecasting the number of elementary school teachers (초등학교 교원 수 예측을 위한 시계열 회귀모형)

  • Ryu, Soo Rack;Kim, Jong Tae
    • Journal of the Korean Data and Information Science Society
    • /
    • v.24 no.2
    • /
    • pp.321-332
    • /
    • 2013
  • Because of the continuous low birthrates, the number of the elementary students will decrease by 17% in 2020 compared to 2011. The purpose of this study is to forecast the number of elementary school teachers until 2020. We used the data in education statistical year books from 1970 to 2010. We used the time-series regression model, time series grouped regression model and exponential smoothing model to predict the number of teachers for the next ten years. Consequently time-series grouped regression model is a better model for forecasting the number of elementary school teachers than other models.

A Study for Traffic Forecasting Using Traffic Statistic Information (교통 통계 정보를 이용한 속도 패턴 예측에 관한 연구)

  • Choi, Bo-Seung;Kang, Hyun-Cheol;Lee, Seong-Keon;Han, Sang-Tae
    • The Korean Journal of Applied Statistics
    • /
    • v.22 no.6
    • /
    • pp.1177-1190
    • /
    • 2009
  • The traffic operating speed is one of important information to measure a road capacity. When we supply the information of the road of high traffic by using navigation, offering the present traffic information and the forecasted future information are the outstanding functions to serve the more accurate expected times and intervals. In this study, we proposed the traffic speed forecasting model using the accumulated traffic speed data of the road and highway and forecasted the average speed for each the road and high interval and each time interval using Fourier transformation and time series regression model with trigonometrical function. We also propose the proper method of missing data imputation and treatment for the outliers to raise an accuracy of the traffic speed forecasting and the speed grouping method for which data have similar traffic speed pattern to increase an efficiency of analysis.