• Title, Summary, Keyword: ARIMA 개입 모형

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Forecasting the Seaborne Trade Volume using Intervention Multiplicative Seasonal ARIMA and Artificial Neural Network Model (개입 승법계절 ARIMA와 인공신경망모형을 이용한 해상운송 물동량의 예측)

  • Kim, Chang-Beom
    • Journal of Korea Port Economic Association
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    • v.31 no.1
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    • pp.69-84
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    • 2015
  • The purpose of this study is to forecast the seaborne trade volume during January 1994 to December 2014 using the multiplicative seasonal autoregressive integrated moving average (ARIMA) along with intervention factors and an artificial neural network (ANN) model. Diagnostic checks of the ARIMA model were conducted using the Ljung-Box Q and Jarque-Bera statistics. All types of ARIMA process satisfied the basic assumption of residuals. The ARIMA(2,1,0) $(1,0,1)_{12}$ model showed the lowest forecast error. In addition, the prediction error of the artificial neural network indicated a level of 5.9% on hidden layer 5, which suggests a relatively accurate forecasts. Furthermore, the ex-ante predicted values based on the ARIMA model and ANN model are presented. The result shows that the seaborne trade volume increases very slowly.

A comparative study on the accuracy of tourism forecasting models (관광객 수요예측모형 정확성 비교연구)

  • Lim, Seong Sik
    • Journal of the Korean Data and Information Science Society
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    • v.29 no.6
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    • pp.1629-1641
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    • 2018
  • This paper presents winters' seasonal exponential smoothing model, seasonal ARIMA model and intervention analysis model as a demand forecasting model for tourists visiting Korea. The tourists is affected by external shock factors such as the tourism policies of the country or the domestic and international economic conditions. The purpose of this study is to find out the factors affecting the forecasting model and to find a time series model to predict the number of foreign tourists who visited Korea more efficiently. As a result of comparing the accuracy of forecasting models through empirical analysis, it was founded that the intervention analysis model reflecting the intervention effect was superior to the seasonal exponential smoothing model or the seasonal ARIMA model.

KTX passenger demand forecast with multiple intervention seasonal ARIMA models (다중개입 계절형 ARIMA 모형을 이용한 KTX 수송수요 예측)

  • Cha, Hyoyoung;Oh, Yoonsik;Song, Jiwoo;Lee, Taewook
    • The Korean Journal of Applied Statistics
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    • v.32 no.1
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    • pp.139-148
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    • 2019
  • This study proposed a multiple intervention time series model to predict KTX passenger demand. In order to revise the research of Kim and Kim (Korean Society for Railway, 14, 470-476, 2011) considering only the intervention of the second phase of Gyeong-bu before November of 2011, we adopted multiple intervention seasonal ARIMA models to model the time series data with additional interventions which occurred after November of 2011. Through the data analysis, it was confirmed that the effects of various interventions such as Gyeong-bu and Ho-nam 2 phase, outbreak of MERS and national holidays, which affected the KTX transportation demand, are successfully explained and the prediction accuracy could be quite improved significantly.

KTX Passenger Demand Forecast with Intervention ARIMA Model (개입 ARIMA 모형을 이용한 KTX 수요예측)

  • Kim, Kwan-Hyung;Kim, Han-Soo
    • Journal of the Korean Society for Railway
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    • v.14 no.5
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    • pp.470-476
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    • 2011
  • This study proposed the intervention ARIMA model as a way to forecast the KTX passenger demand. The second phase of the Gyeongbu high-speed rail project and the financial crisis in 2008 were analyzed in order to determine the effect of time series on the opening of a new line and economic impact. As a result, the financial crisis showed that there is no statistically significant impact, but the second phase of the Gyeongbu high-speed rail project showed that the weekday trips increased about 17,000 trips/day and the weekend trips increased about 26,000 trips/day. This study is meaningful in that the intervention explained the phenomena affecting the time series of KTX trip and analyzed the impact on intervention of time series quantitatively. The developed model can be used to forecast the outline of the overall KTX demand and to validate the KTX O/D forecasting demand.

Forecasts of the 2011-BDI Using the ARIMA-Type Models (ARIMA모형을 이용한 2011년 BDI의 예측)

  • Mo, Soo-Won
    • Journal of Korea Port Economic Association
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    • v.26 no.4
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    • pp.207-218
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    • 2010
  • The purpose of the study is to predict the shipping business during the period of 2011 using the ARIMA-type models. This include the ARIMA and Intervention-ARIMA models. The multivariate cause-effect econometric model is not employed for not assuring a higher degree of forecasting accuracy than the univariate variable model. Such a cause-effect econometric model also fails in adjusting itself for the post-sample. This article introduces the four ARIMA models and six Intervention-ARIMA models. The monthly data cover the period January 2000 through October 2010. The out-of-sample forecasting performance is compared between the ARIMA-type models and the random walk model. Forecasting performance is measured by three summary statistics: root mean squared percent error, mean absolute percent error and mean percent error. The root mean squared percent errors of all the ARIMA-type models are somewhat higher than normally expected. Furthermore, the random walk model outperforms all the ARIMA-type models. This reveals that the BDI is just a random walk phenomenon and it's meaningless to predict the BDI using various econometric techniques. The ARIMA-type models show that the shipping market is expected to be bearish in 2011. These pessimistic ex-ante forecasts are supported by the Hodrick-Prescott filtering technique.

Forecasting the BDI during the Period of 2012 (2012 BDI의 예측)

  • Mo, Soo-Won
    • Journal of Korea Port Economic Association
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    • v.27 no.4
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    • pp.1-11
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    • 2011
  • In much the same way as the US Lehman crisis of 2008-2009 severely impacted the European economy through financial market dislocation, a European banking crisis would materially impact the US economy through a generalized increase in global risk aversion. A deepening of the European crisis could very well derail the US economic recovery and have a harmful impact on the Asian economies. This kind of vicious circle could be a bad news to the shipping companies. The purpose of the study is to predict the Baltic Dry Index representing the shipping business during the period of 2012 using the ARIMA-type models. This include the ARIMA and Intervention-ARIMA models. This article introduces the four ARIMA models and six Intervention-ARIMA models. The monthly data cover the period January 2000 through October 2011. The out-of-sample forecasting performance is also calculated. Forecasting performance is measured by three summary statistics: root mean squared percent error, mean absolute percent error and mean percent error. The root mean squared percent errors, however, are somewhat higher than normally expected. This reveals that it is very difficult to predict the BDI The ARIMA-type models show that the shipping market will be bearish in 2012. These pessimistic ex-ante forecasts are supported by the Hodrick-Prescott filtering technique.

Forecasts of the BDI in 2010 -Using the ARIMA-Type Models and HP Filtering (2010년 BDI의 예측 -ARIMA모형과 HP기법을 이용하여)

  • Mo, Soo-Won
    • Journal of Korea Port Economic Association
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    • v.26 no.1
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    • pp.222-233
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    • 2010
  • This paper aims at predicting the BDI from Jan. to Dec. 2010 using such econometric techniues of the univariate time series as stochastic ARIMA-type models and Hodrick-Prescott filtering technique. The multivariate cause-effect econometric model is not employed for not assuring a higher degree of forecasting accuracy than the univariate variable model. Such a cause-effect econometric model also fails in adjusting itself for the post-sample. This article introduces the two ARIMA models and five Intervention-ARIMA models. The monthly data cover the period January 2000 through December 2009. The out-of-sample forecasting performance is compared between the ARIMA-type models and the random walk model. Forecasting performance is measured by three summary statistics: root mean squared error (RMSE), mean absolute error (MAE) and mean error (ME). The RMSE and MAE indicate that the ARIMA-type models outperform the random walk model And the mean errors for all models are small in magnitude relative to the MAE's, indicating that all models don't have a tendency of overpredicting or underpredicting systematically in forecasting. The pessimistic ex-ante forecasts are expected to be 2,820 at the end of 2010 compared with the optimistic forecasts of 4,230.

Intervention Analysis of Korea Tourism Data (개입모형을 이용한 한국의 입출국자 수의 분석)

  • Kim, Su-Yong;Seong, Byeong-Chan
    • The Korean Journal of Applied Statistics
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    • v.24 no.5
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    • pp.735-743
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    • 2011
  • This study analyzes inbound and outbound Korea tourism data through an intervention model. For the analysis, we adopt three intervention factors: (1) IMF bailout crisis in December 1997, (2) Severe Acute Respiratory Syndrome(SARS) outbreak in March 2003, and (3) Lehman Brothers bankruptcy in September 2008. The empirical results show that only the SARS factor lowered inbound tourism from April 2003 with a drastic decline in May 2003 and gradually decaying since then. However, all three factors significantly lowered tourism in the case of outbound tourism. Especially, the effect of the IMF is shown to be permanent from December 1997 and the effects of SARS and the Lehman Brothers bankruptcy abrupt and temporary with a gradual decay.

Prediction of Electricity Sales by Time Series Modelling (시계열모형에 의한 전력판매량 예측)

  • Son, Young Sook
    • The Korean Journal of Applied Statistics
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    • v.27 no.3
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    • pp.419-430
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    • 2014
  • An accurate prediction of electricity supply and demand is important for daily life, industrial activities, and national management. In this paper electricity sales is predicted by time series modelling. Real data analysis shows the transfer function model with cooling and heating days as an input time series and a pulse function as an intervention variable outperforms other time series models for the root mean square error and the mean absolute percentage error.