• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1201-1210
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• 2019

Stock price is characterized as being mutable, non-linear and stochastic. These key characteristics are known to have a direct influence on the stock markets globally. Given that the stock price data often contain both linear and non-linear patterns, no single model can be adequate in modelling and predicting time series data. The autoregressive integrated moving average (ARIMA) model cannot deal with non-linear relationships, however, it provides an accurate and effective way to process autocorrelation and non-stationary data in time series forecasting. On the other hand, the neural network provides an effective prediction of non-linear sequences. As a result, in this study, we used a hybrid ARIMA and neural network model to forecast the monthly closing price of the Shanghai composite index and Shenzhen component index.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.1516-1519
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• 2011

예측에 필요한 중요한 자료에는 비선형 자료와 시계열과 같은 선형 자료 등이 있다. 이들 자료는 그 함축적인 관계가 매우 복잡하여 전통적인 통계분석 도구로 식별하는데 어려움이 많다. 신경망 분석은 비모수적 문제나 비선형 곡선 적합능력의 우수성 때문에 현실세계에서의 고유한 복잡성을 다루는 많은 경제 응용 분야에서 널리 이용되고 있다. 신경망은 또한 경제 시계열자료의 예측분야에서도 여러 연구에서 훌륭한 도구로서 적용되고 있다. 전통적으로 우리나라에서 시계열자료의 예측은 선형 자료적 분석을 중심으로 하는 분석도구인 자기회귀누적이동평균(ARIMA)모형을 이용한 시계열분석이 일반적이다. 이 연구에서는 신경망과 ARIMA 모형을 이용하여 한국의 주가변동 자료 및 자동차등록 현황 자료등과 같은 시계열자료를 이용한 예측결과를 비교한다. 연구의 결과는 신경망을 이용한 예측 방법들이 ARIMA 예측 결과보다 통계적으로 작은 오차를 주는 보다 효율적인 방법임을 보여주고 있다.

• Environmental Engineering Research
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• v.22 no.2
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• pp.162-168
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• 2017

Rainfall is one of the most important phenomena of the natural system. In Bangladesh, agriculture largely depends on the intensity and variability of rainfall. Therefore, an early indication of possible rainfall can help to solve several problems related to agriculture, climate change and natural hazards like flood and drought. Rainfall forecasting could play a significant role in the planning and management of water resource systems also. In this study, univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) model was used to forecast monthly rainfall for twelve months lead-time for thirty rainfall stations of Bangladesh. The best SARIMA model was chosen based on the RMSE and normalized BIC criteria. A validation check for each station was performed on residual series. Residuals were found white noise at almost all stations. Besides, lack of fit test and normalized BIC confirms all the models were fitted satisfactorily. The predicted results from the selected models were compared with the observed data to determine prediction precision. We found that selected models predicted monthly rainfall with a reasonable accuracy. Therefore, year-long rainfall can be forecasted using these models.

• The Korean Journal of Applied Statistics
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• v.37 no.3
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• pp.297-309
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• 2024

This paper evaluates the performance of the neural network autoregressive model combined with an exponential smoothing model, called the NNARX+ETS model. The combined model utilizes the components of ETS as exogenous variables for NNARX, to forecast time series data using artificial neural networks. The main idea is to enhance the performance of NNAR using only lags of the original time series data, by combining traditional time series analysis methods with the neural networks through NNARX. We employ two real data for performance evaluation and compare the NNARX+ETS with NNAR and traditional time series analysis methods such as ETS and ARIMA (autoregressive integrated moving average) models.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.4
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• pp.1-10
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• 2013

This study was conducted to investigate that bus information was used as an information of travel speed. To determine the travel speed on the road, bus information and the information collected from the point detector and the interval detection installed were compared. If bus information has the function of traffic information detector, can provide the travel speed information to road users. To this end, the model of recognizing the traffic patterns is necessary. This study used simple moving-average method, simple exponential smoothing method, Double moving average method, Double exponential smoothing method, ARIMA(Autoregressive integrated moving average model) as the existing methods rather than new approach methods. This study suggested the possibility to replace bus information system into other information collection system.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.819-821
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• 2005

Since the System Marginal Price (SMP) is a vital factor to the market entities who intend to maximize the their profit, the short-term marginal price forecasting should be performed correctly. In a electricity market, the short-term trading between the market entities can be generally affected a short-term market price. Therefore, the exact forecasting of SMP can influence on the profit of market participants. This paper presents a methodology of day-ahead SMP foretasting using Autoregressive Integrated Moving Average (ARIMA). To show the efficiency and effectiveness of the proposed method, the numerical studies have been performed using historical data of SMP in 2004.

• Journal of Biosystems Engineering
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• v.38 no.1
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• pp.9-17
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• 2013

Purpose: The goal of this study was to develop a methodology for the demand forecast of tractor, riding type rice transplanter and combine harvester using an ARIMA (autoregressive integrated moving average) model, one of time series analysis methods, and to forecast their demands from 2012 to 2021 in South Korea. Methods: To forecast the demands of three kinds of machines, ARIMA models were constructed by following three stages; identification, estimation and diagnose. Time series used were supply and stock of each machine and the analysis tool was SAS 9.2 for Windows XP. Results: Six final models, supply based ones and stock based ones for each machine, were constructed from 32 tentative models identified by examining the ACF (autocorrelation function) plots and the PACF (partial autocorrelation function) plots. All demand series forecasted by the final models showed increasing trends and fluctuations with two-year period. Conclusions: Some forecast results of this study are not applicable immediately due to periodic fluctuation and large variation. However, it can be advanced by incorporating treatment of outliers or combining with another forecast methods.

• Nuclear Engineering and Technology
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• v.49 no.5
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• pp.1063-1070
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• 2017

This paper presents a method for forecasting future uranium prices that is used as input data to calculate the uranium cost, which is a rational key cost driver of the nuclear fuel cycle cost. In other words, the statistical autoregressive integrated moving average (ARIMA) model and existing engineering cost estimation method, the so-called escalation rate model, were subjected to a comparative analysis. When the uranium price was forecasted in 2015, the margin of error of the ARIMA model forecasting was calculated and found to be 5.4%, whereas the escalation rate model was found to have a margin of error of 7.32%. Thus, it was verified that the ARIMA model is more suitable than the escalation rate model at decreasing uncertainty in nuclear fuel cycle cost calculation.

• 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.

• Journal of Korea Port Economic Association
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• v.23 no.1
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• pp.149-162
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• 2007

The purpose of this study is to forecast the marine trading volumes using multiplicative seasonal Autoregressive Integrated Moving Average(ARIMA) model. The paper proceeds by comparing the forecasting performances of the unload volumes with those of the load volumes with Box-Jenkins ARIMA model. Also, I present the predicted values based on the ARIMA model. The result shows that the trading volumes increase very slowly.