• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.3
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• pp.75-82
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• 2022

The purpose of the Consumer sentiment index survey is to determine the consumer's economic situation and consumption spending plan, and it is used as basic data for diagnosing economic phenomena and forecasting the future economic direction. The purpose of this paper is to analyze and predict the future Consumer sentiment index using the ARMA model based on the past consumer index. Consumer sentiment index is determined according to consumer trends, so it can reflect consumer realities. The consumer sentiment index is greatly influenced by economic indicators such as the base interest rate and consumer price index, as well as various external economic factors. If the consumer sentiment index, which fluctuates greatly due to consumer economic conditions, can be predicted, it will be useful information for households, businesses, and policy authorities. This study predicted the Consumer sentiment index for the next 3 years (36 months in total) by using time series analysis using the ARMA model. As a result of the analysis, it shows a characteristic of repeating an increase or a decrease every month according to the consumer trend. This study provides empirical results of prediction of Consumer sentiment index through statistical techniques, and has a contribution to raising the need for policy authorities to prepare flexible operating policies in line with economic trends.

• Korea Trade Review
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• v.47 no.3
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• pp.211-232
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• 2022

Due to the impact of the public health event COVID-19 epidemic, the Chinese futures market showed "Black Swan". This has brought the unpredictable into the economic environment with many commodities falling by the daily limit, while gold performed well and closed in the sunshine(Yan-Li and Rui Qian-Wang, 2020). Volatility is integral part of financial market. As an emerging market and a special precious metal, it is important to forecast return of gold futures price. This study selected data of the SHFE gold futures returns and conducted an empirical analysis based on the generalised autoregressive conditional heteroskedasticity (GARCH)-type model. Comparing the statistics of AIC, SC and H-QC, ARMA (12,9) model was selected as the best model. But serial correlation in the squared returns suggests conditional heteroskedasticity. Next part we established the autoregressive moving average ARMA-GARCH-type model to analysis whether Volatility Clustering and the leverage effect exist in the Chinese gold futures market. we consider three different distributions of innovation to explain fat-tailed features of financial returns. Additionally, the error degree and prediction results of different models were evaluated in terms of mean squared error (MSE), mean absolute error (MAE), Theil inequality coefficient(TIC) and root mean-squared error (RMSE). The results show that the ARMA(12,9)-TGARCH(2,2) model under Student's t-distribution outperforms other models when predicting the Chinese gold futures return series.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.35 no.10
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• pp.438-444
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• 1986

The signal can be modeled as a linear combination of its past values and present and past values of a hypothetical input to system whose output is given signal. Using this model spectral estimation problem can be reduced to estimate the ARMA parameters. This paper presents recursive modified instrumental variable algorithm which can estimate AR and MA parameters. For more accurate estimation, overdetermined modified IV algorithm is also derived. Computer simulations are presented to illustrate the above methods.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1998.10a
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• pp.665-670
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• 1998

본 연구에서는 시계열자료의 ARMA 모형화를 위해 의사결정트리 분류기상에 존재하는 인공신경망의 구조를 개선하여 이들 각각의 인공신경망으로부터 도출된 결과를 Dempster's rule of combination을 이용하여 결합할 수 있는 방법론을 제시하고 있다. 인공신경망을 이용한 기존의 ARMA 모형화 방법과 비교한 결과, 본 연구에서는 제시한 방법이 주어진 ESACF 특성패턴에 대해 보다 정확하게 ARMA 모형화를 하는 것으로 나타났다.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1983.04a
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• pp.16-17
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• 1983

In Speech signal processing, ARMA spectral estimation method is used. It has been demonstrated that the ARMA model provides better spectral estimation then the more specialized AR model and MA model. Dynamic program is used to achieve time algnment. Speech sound similarity is defined to be proportional to the distance seperating to sound in a vector space defined by ARMA model. AS a result, the recognition rate of 97.3% for three speaker is obtained.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.4
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• pp.380-385
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• 1990

This Paper presents a general algorithm for the parameter estimation of an antoregressive moving average process observed in additive white noise. The algorithm is based on the Gauss-Newton recursive prediction error method. For the parameter estimation, the output measurement is modelled as an innovation process using the spectral factorization, so that noise free RPE ARMA estimation can be used. Using apriori known properties leads to algorithm with smaller computation and better accuracy be the parsimony principle. Computer simulation examples show the effectiveness of the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1903-1906
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• 2005

We proposed a new variant of correlation approach for ARMA model. The proposed method is is intended to make the current prediction error uncorrelated with the past one. In the investigation of the properties, the uniqueness, consistency and asymptotic normality of the estimate are shown. Via simulation results, we show that the proposed method give good estimates for various systems.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.225-230
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• 1988

In this paper, a new method for determining the order of AR, ARMA processes based on PLS (predictive least square) principle is proposed, This method using modified lattice algorithm which has additional step is amenable to on-line or adaptive operation and is more accurate than any other mpthod. Some computer simulations are presented to show the efficiency of the proposed algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.446-450
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• 1987

In this paper, overdetermined method is used for high resolution spectral estimation in case of short data record length. To reduce the computational effort and to obtain recursive form of estimation algorithm, we modify data matrix to have near-Toeplitz structure. Then, new recursive algorithm is derived in the form of fast Kalman algorithm. Two stage procedure is used for the estimation of ARMA parameters. First AR parameters are estimated by using overdetermined modified Yule-walker equation, and then MA parameters are implicitly estimated by estimating numerator spectral coefficients(NS).

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.442-445
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• 1987

In this paper, a new ARMA spectrum estimation algorithm based on Circular Lattice filter is presented. Since APMA model is used in signal modeling, high-resolution spectrum can be obtained. And the computational burden is reduced by using Circular Lattice filter. By modifying the input estimation part of other proposed methods, we can get high-resolution spectrum with less computation and less memory compared with other Lattice methods. Some computer simulations are performed.