• Title/Summary/Keyword: ARIMA Forecasting

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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.

Time Series Analysis of Patent Keywords for Forecasting Emerging Technology (특허 키워드 시계열 분석을 통한 부상 기술 예측)

  • Kim, Jong-Chan;Lee, Joon-Hyuck;Kim, Gab-Jo;Park, Sang-Sung;Jang, Dong-Sick
    • KIPS Transactions on Software and Data Engineering
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    • v.3 no.9
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    • pp.355-360
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    • 2014
  • Forecasting of emerging technology plays important roles in business strategy and R&D investment. There are various ways for technology forecasting including patent analysis. Qualitative analysis methods through experts' evaluations and opinions have been mainly used for technology forecasting using patents. However qualitative methods do not assure objectivity of analysis results and requires high cost and long time. To make up for the weaknesses, we are able to analyze patent data quantitatively and statistically by using text mining technique. In this paper, we suggest a new method of technology forecasting using text mining and ARIMA analysis.

A Study on the Demand Forecasting and Efficient Operation of Jeju National Airport using seasonal ARIMA model (계절 ARIMA 모형을 이용한 제주공항 여객 수요예측 및 효율적 운영에 관한 연구)

  • Kim, Kyung-Bum;Hwang, Kyung-Soo
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.13 no.8
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    • pp.3381-3388
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    • 2012
  • This research is to find out the method appropriate for the forecasting of passennger demand using seasonal ARIMA model and efficient operation in Jeju National Airport. Time series monthly data for the investigation were collected ranging from January 2003 to December 2011. A total of 108 observations were used for data analysis. Research findings showed that the multiplicative seasonal ARIMA(0.1.2)(0.1.1)12 model is appropriate model. The number of passengers in Jeju National Airport will continue to rise, it was expected to surpass 20 million people.

Comparison of the BOD Forecasting Ability of the ARIMA model and the Artificial Neural Network Model (ARIMA 모형과 인공신경망모형의 BOD예측력 비교)

  • 정효준;이홍근
    • Journal of Environmental Health Sciences
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    • v.28 no.3
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    • pp.19-25
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    • 2002
  • In this paper, the water quality forecast was performed on the BOD of the Chungju Dam using the ARIMA model, which is a nonlinear statistics model, and the artificial neural network model. The monthly data of water quality were collected from 1991 to 2000. The most appropriate ARIMA model for Chungju dam was found to be the multiplicative seasonal ARIMA(1,0,1)(1,0,1)$_{12}$, model. While the artificial neural network model, which is used relatively often in recent days, forecasts new data by the strength of a learned matrix like human neurons. The BOD values were forecasted using the back-propagation algorithm of multi-layer perceptrons in this paper. Artificial neural network model was com- posed of two hidden layers and the node number of each hidden layer was designed fifteen. It was demonstrated that the ARIMA model was more appropriate in terms of changes around the overall average, but the artificial neural net-work model was more appropriate in terms of reflecting the minimum and the maximum values.s.

Time Series Analysis and Development of Forecasting Model in Apartment House Cost Using X-12 ARIMA (X-12 ARIMA를 이용한 아파트 원가의 변동분석 및 예측모델 개발)

  • Cho, Hun-Hee
    • Korean Journal of Construction Engineering and Management
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    • v.6 no.6 s.28
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    • pp.98-106
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    • 2005
  • The construction cost index and the forecasting model of apartment house can be efficient for evaluating the validness of the fluctuating price, and for making guidelines for construction firms when calculating their profit. In this study the previous construction cost index of apartment house was improved, and the forecasting model based on X-12 ARIMA was developed. According to the result, during the last five years the construction cost, excluding labor expense, has risen approximately to 22.7%. And during next three years, additional 16.8% rise of construction cost is expected. Those quantitative results can be utilized for evaluating the apartment house's selling price in an indirection, and be helpful to understand the variation pattern of the price.

Development of SMP Forecasting Method Using ARIMA Model (ARIMA 모형을 이용한 계통한계가격 예측 방법론 개발)

  • Kim, Dae-Yong;Lee, Chan-Joo;Park, Jong-Bae;Shin, Joong-Rin;Chun, Yeong-Han
    • Proceedings of the KIEE Conference
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    • 2005.11b
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    • pp.148-150
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    • 2005
  • Since the SMP(System Marginal Price) is a vital factor to the market participants who intend to maximize the their profit and to the ISO(Independent System Operator) who wish to operate the electricity market in a stable sense, the short-term marginal price forecasting should be performed correctly. This paper presents a methodology of a day-ahead SMP forecasting using ARIMA(Autoregressive Integrated Moving Average) based on the Time Series. And also we suggested a correction algorithm to minimize the forecasting error in order to improve efficiency and accuracy of the SMP forecasting. To show the efficiency and effectiveness of the proposed method, the numerical studies have been performed using Historical data of SMP in 2004 published by KPX(Korea Power Exchange).

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Traffic-Flow Forecasting using ARIMA, Neural Network and Judgment Adjustment (신경망, 시계열 분석 및 판단보정 기법을 이용한 교통량 예측)

  • Jang, Seok-Cheol;Seok, Sang-Mun;Lee, Ju-Sang;Lee, Sang-Uk;An, Byeong-Ha
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 2005.05a
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    • pp.795-797
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    • 2005
  • During the past few years, various traffic-flow forecasting models, i.e. an ARIMA, an ANN, and so on, have been developed to predict more accurate traffic flow. However, these models analyze historical data in an attempt to predict future value of a variable of interest. They make use of the following basic strategy. Past data are analyzed in order to identify a pattern that can be used to describe them. Then this pattern is extrapolated, or extended, into the future in order to make forecasts. This strategy rests on the assumption that the pattern that has been identified will continue into the future. So ARIMA or ANN models with its traditional architecture cannot be expected to give good predictions unless this assumption is valid; The statistical models in particular, the time series models are deficient in the sense that they merely extrapolate past patterns in the data without reflecting the expected irregular and infrequent future events Also forecasting power of a single model is limited to its accurate. In this paper, we compared with an ANN model and ARIMA model and tried to combine an ARIMA model and ANN model for obtaining a better forecasting performance. In addition to combining two models, we also introduced judgmental adjustment technique. Our approach can improve the forecasting power in traffic flow. To validate our model, we have compared the performance with other models. Finally we prove that the proposed model, i.e. ARIMA + ANN + Judgmental Adjustment, is superior to the other model.

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Application to Evaluation of Hydrologic Time Series Forecasting for Long-Term Runoff Simulation (장기유출모의를 위한 수문시계열 예측모형의 적용성 평가)

  • Yoon, Sun-Kwon;Ahn, Jae-Hyun;Kim, Jong-Suk;Moon, Young-Il
    • Journal of Korea Water Resources Association
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    • v.42 no.10
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    • pp.809-824
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    • 2009
  • Hydrological system forecasting, which is the short term runoff historical data during the limited period in dam site, is a conditional precedent of hydrological persistence by stochastic analysis. We have forecasted the monthly hydrological system from Andong dam basin data that is the rainfall, evaporation, and runoff, using the seasonal ARIMA (autoregressive integrated moving average) model. Also we have conducted long term runoff simulations through the forecasted results of TANK model and ARIMA+TANK model. The results of analysis have been concurred to the observation data, and it has been considered for application to possibility on the stochastic model for dam inflow forecasting. Thus, the method presented in this study suggests a help to water resource mid- and long-term strategy establishment to application for runoff simulations through the forecasting variables of hydrological time series on the relatively short holding runoff data in an object basins.

Analysis of Price Forecasting and Goodness-of-Fit of the Metals Extracted from Deep Seabed Manganese Nodules (심해저 망간단괴에서 추출되는 금속가격 예측 및 적합도 분석)

  • Kwon, Suk-Jae;Jeong, Sun-Young
    • Ocean and Polar Research
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    • v.36 no.4
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    • pp.505-514
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    • 2014
  • The development of deep seabed manganese nodules has been carried out with the aim of commercial development in 2023. It is important to forecast the price of the four metals (copper, nickel, cobalt, and manganese) extracted from manganese nodules because price change is a criterion for investment decision. The main purpose of the study is to forecast the price of four metals using the ARIMA model and VAR model, and calculate the MAPE to compare a goodness-of-fit between the two models. The estimated results of the two models reveal statistical significance and are in keeping with economic theory. The results of MAPE for goodness-of-fit show that the VAR model is between 0.1 and 0.2, and the ARIMA model is between 0.4 and 0.6. That is, the VAR model is better than the ARIMA model in forecasting changes in the price of metals.

A Study on Air Demand Forecasting Using Multivariate Time Series Models (다변량 시계열 모형을 이용한 항공 수요 예측 연구)

  • Hur, Nam-Kyun;Jung, Jae-Yoon;Kim, Sahm
    • The Korean Journal of Applied Statistics
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    • v.22 no.5
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    • pp.1007-1017
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    • 2009
  • Forecasting for air demand such as passengers and freight has been one of the main interests for air industries. This research has mainly focus on the comparison the performance between the univariate seasonal ARIMA models and the multivariate time series models. In this paper, we used real data to predict demand on international passenger and freight. And multivariate time series models are better than the univariate models based on the accuracy criteria.