• Title, Summary, Keyword: ARIMA Intervention Model

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

  • Kim, Kwan-Hyung;Kim, Han-Soo;Lee, Sung-Duk;Lee, Hyun-Gi;Yoon, Kyoung-Man
    • Proceedings of the KSR Conference
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    • pp.1715-1721
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    • 2011
  • For an efficient railroad operations the demand forecasting is required. Time series models can quickly forecast the future demand with fewer data. As well as the accuracy of forecasting is excellent compared to other methods. In this study is proposed the intervention ARIMA model for forecasting methods of KTX passenger demand. The intervention ARIMA model may reflect the intervention such as the Kyongbu high-speed rail project second phase. The simple seasonal ARIMA model is predicted to overestimate the KTX passenger demand. However, intervention ARIMA model is predicted the reasonable results. The KTX passenger demands were predicted to be a week units separated by the weekday and weekend.

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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.

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 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.

Optimal Forecasting for Sales at Convenience Stores in Korea Using a Seasonal ARIMA-Intervention Model (계절형 ARIMA-Intervention 모형을 이용한 한국 편의점 최적 매출예측)

  • Jeong, Dong-Bin
    • Journal of Distribution Science
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    • v.14 no.11
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    • pp.83-90
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    • 2016
  • Purpose - During the last two years, convenient stores (CS) are emerging as one of the most fast-growing retail trades in Korea. The goal of this work is to forecast and to analyze sales at CS using ARIMA-Intervention model (IM) and exponential smoothing method (ESM), together with sales at supermarkets in South Korea. Considering that two retail trades above are homogeneous and comparable in size and purchasing items on off-line distribution channel, individual behavior and characteristic can be detected and also relative superiority of future growth can be forecasted. In particular, the rapid growth of sales at CS is regarded as an everlasting external event, or step intervention, so that IM with season variation can be examined. At the same time, Winters ESM can be investigated as an alternative to seasonal ARIMA-IM, on the assumption that the underlying series shows exponentially decreasing weights over time. In case of sales at supermarkets, the marked intervention could not be found over the underlying periods, so that only Winters ESM is considered. Research Design, Data, and Methodology - The dataset of this research is obtained from Korean Statistical Information Service (1/2010~7/2016) and Survey of Service Trend of Korea Statistics Administration. This work is exploited time series analyses such as IM, ESM and model-fitting statistics by using TSPLOT, TSMODEL, EXSMOOTH, ARIMA and MODELFIT procedures in SPSS 23.0. Results - By applying seasonal ARIMA-Intervention model to sales at CS, the steep and persisting increase can be expected over the next one year. On the other hand, we expect the rate of sales growth of supermarkets to be lagging and tied up constantly in the next 2016 year. Conclusions - Based on 2017 one-year sales forecasts for CS and supermarkets, we can yield the useful information for the development of CS and also for all retail trades. Future study is needed to analyze sales of popular items individually such as tobacco, banana milk, soju and so on and to get segmented results. Furthermore, we can expand sales forecasts to other retail trades such as department stores, hypermarkets, non-store retailing, so that comprehensive diagnostics can be delivered in the future.

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.

A Study on the Air Travel Demand Forecasting using time series ARIMA-Intervention Model (ARIMA-Intervention 시계열모형을 활용한 제주 국내선 항공여객수요 추정)

  • Kim, Min-Su;Kim, Kee-Woong;Park, Sung-Sik
    • Journal of the Korean Society for Aviation and Aeronautics
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    • v.20 no.1
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    • pp.66-75
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    • 2012
  • The purpose of this study is to analyze the effect of intervention variables which may affect the air travel demand for Jeju domestic flights and to anticipate the air travel demand for Jeju domestic flights. The air travel demand forecasts for Jeju domestic flights are conducted through ARIMA-Intervention Model selecting five intervention variables such as 2002 World Cup games, SARS, novel swine-origin influenza A, Yeonpyeongdo bombardment and Japan big earthquake. The result revealed that the risk factor such as the threat of war that is a negative intervention incident and occurred in Korea has the negative impact on the air travel demand due to the response of risk aversion by users. However, when local natural disasters (earthquakes, etc) occurring in neighboring courtiers and global outbreak of an epidemic gave the negligible impact to Korea, negative intervention incident would have a positive impact on air travel demand as a response to find alternative due to rational expectation of air travel customers. Also we realize that a mega-event such as the 2002 Korea-Japan World Cup games reduced the air travel demand in a short-term period unlike the perception in which it will increase the air travel demand and travel demands in the corresponding area.

A study on demand forecasting for Jeju-bound tourists by travel purpose using seasonal ARIMA-Intervention model (계절형 ARIMA-Intervention 모형을 이용한 여행목적 별 제주 관광객 수 예측에 관한 연구)

  • Song, Junmo
    • Journal of the Korean Data and Information Science Society
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    • v.27 no.3
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    • pp.725-732
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    • 2016
  • This study analyzes the number of Jeju-bound tourists according to travellers' purposes. We classify the travellers' purposes into three categories: "Rest and Sightseeing", "Leisure and Sport", and "Conference and Business". To see an impact of MERS outbreak occurred in May 2015 on the number of tourists, we fit seasonal ARIMA-Intervention model to the monthly arrivals data from January 2005 to March 2016. The estimation results show that the number of tourists for "Leisure and Sport" and "Conference and Business" were significantly affected by MERS outbreak whereas arrivals for "Rest and Sightseeing" were little influenced. Using the fitted models, we predict the number of Jeju-bound tourists.

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.

Effects of maximum speed limit on Gyeongbu Expressway (경부고속도로 최고제한속도 상향에 따른 교통사고 영향 분석)

  • Song, Yinhua;Seong, Byeongchan
    • The Korean Journal of Applied Statistics
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    • v.30 no.5
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    • pp.719-731
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    • 2017
  • In September 2010, the Korea government increased the speed limit on the Gyeongbu Expressway (Cheonan IC.-Yangjae IC) from 100 to 110 km per hour. This paper considers ARIMA-Intervention model to analyze the effects of the speed limit change on the incidences of traffic accidents and injuries. In addition, in order to investigate the effects more clearly, we also analyze the difference between the two lines of Cheonan IC-Yangjae IC and Busan IC-Cheonan IC. As a result, we observe that the numbers of accidents and injuries have increased after the speed limit change. The increases are strikingly distinctive in comparison to other lines (Busan IC-Cheonan IC) where there have been no changes in the maximum speed limit.