• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1221-1226
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• 2006

The use of Artificial Neural Networks (ANN) has received increasing attention in the analysis and prediction of financial time series. Stationarity of the observed financial time series is the basic underlying assumption in the practical application of ANN on financial time series. In this paper, we will investigate whether it is feasible to relax the stationarity condition to non-stationary time series. Our result discusses the range of complexities caused by non-stationary behavior and finds that overfitting by ANN could be useful in the analysis of such non-stationary complex financial time series.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.7
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• pp.289-302
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• 2019

Structural change of time series means that the distribution of observations is relatively stable in the period of constituting the entire time series data, but shows a sudden change of the distribution characteristic at a specific time point. Within a non-stationary long-term time series, it is important to determine in a timely manner whether the change in short-term trends is transient or structurally changed. This is because it is necessary to always detect the change of the time series trend and to take appropriate measures to cope with the change. In this paper, we propose a method for decision makers to easily grasp the structural changes of time series by visualizing the test results based on the unit root test. Particularly, it is possible to grasp the short-term structural changes even in the long-term time series through the method of dividing the time series and testing it.

• Wind and Structures
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• v.26 no.3
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• pp.129-146
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• 2018

Extreme wind speed analysis has been carried out conventionally by assuming the extreme series data is stationary. However, time-varying trends of the extreme wind speed series could be detected at many surface meteorological stations in China. Two main reasons, exposure change and climate change, were provided to explain the temporal trends of daily maximum wind speed and annual maximum wind speed series data, recorded at Hangzhou (China) meteorological station. After making a correction on wind speed series for time varying exposure, it is necessary to perform non-stationary statistical modeling on the corrected extreme wind speed data series in addition to the classical extreme value analysis. The generalized extreme value (GEV) distribution with time-dependent location and scale parameters was selected as a non-stationary model to describe the corrected extreme wind speed series. The obtained non-stationary extreme value models were then used to estimate the non-stationary extreme wind speed quantiles with various mean recurrence intervals (MRIs) considering changing climate, and compared to the corresponding stationary ones with various MRIs for the Hangzhou area in China. The results indicate that the non-stationary property or dependence of extreme wind speed data should be carefully evaluated and reflected in the determination of design wind speeds.

• Journal of the Korean Statistical Society
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• v.23 no.2
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• pp.504-512
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• 1994

A simple proof for the random central limit theorem is given for a family of stationary linear lattice processes, which belogn to a class of 2 dimensional random fields, applying the Beveridge and Nelson decomposition in time series context. The result is an extension of Fakhre-Zakeri and Fershidi (1993) dealing with the linear process in time series to the case of the linear lattice process with 2 dimensional indices.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.2
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• pp.65-73
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• 2013

In the simulation output analysis, bootstrap method is an applicable resampling technique to insufficient data which are not significant statistically. The moving block bootstrap, the stationary bootstrap, and the threshold bootstrap are typical bootstrap methods to be used for autocorrelated time series data. They are nonparametric methods for stationary time series data, which correctly describe the original data. In the simulation output analysis, however, we may not use them because of the non-stationarity in the data set caused by the trend such as increasing or decreasing. In these cases, we can get rid of the trend by differencing the data, which guarantees the stationarity. We can get the bootstrapped data from the differenced stationary data. Taking a reverse transform to the bootstrapped data, finally, we get the pseudo-samples for the original data. In this paper, we introduce the applicability of bootstrap methods to the time series data having trend, and then verify it through the statistical analyses.

• Journal of the Korean Data and Information Science Society
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• v.18 no.1
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• pp.73-80
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• 2007

Cointegration(together with VARMA(vector ARMA)) has been proven to be useful for analyzing multivariate non-stationary data in the field of financial time series. It provides a linear combination (which turns out to be stationary series) of non-stationary component series. This linear combination equation is referred to as long term equilibrium between the component series. We consider two sets of Korean bivariate financial time series and then illustrate cointegration analysis. Specifically estimated VAR(vector AR) and VECM(vector error correction model) are obtained and CV(cointegrating vector) is found for each data sets.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.2
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• pp.168-175
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• 1999

In this paper, a modified RBF(Radial Basis Function) network structure is suggested for the prediction of a time-series with non-linear, non-stationary characteristics. Coventional RBF network predicting time series by using past outputs sense the trajectory of the time series and react when there exists strong relation between input and hidden activation function's RBF center. But this response is highly sensitive to level and trend of time serieses. In order to overcome such dependencies, hidden activation functions are modified to react to the increments of input variable and multiplied by increment(or dectement) for prediction. When the suggested structure is applied to prediction of Macyey-Glass chaotic time series, Lorenz equation, and Rossler equation, improved performances are obtained.

• Journal of agriculture & life science
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• v.45 no.5
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• pp.115-126
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• 2011

This paper estimates the number of Korean cattle using time-series ARIMA model. This study classifies the structure of the number of cattle into six indexes to reflect the characteristics of cattle. This study apply ARIMA model to these six indexes according to Box-Jenkins procedure to identify, estimate and predict. The rates of slaughter for aged female and aged male cow is analyzed as non-stationary time series which has unit roots and other 4 indexes is analyzed as stationary time series. The differencing is applied to get rid of non-stationarity for the non-stationary time series. The results show that the number of cattle will be reduced from 2012 as a higher point and rebounded from 2018 as a lower point.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.611-622
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• 2018

Observed climate data are processed under the assumption that their time series are stationary, as in multi-step temperature and precipitation prediction, which usually leads to low prediction accuracy. If a climate system model is based on a single prediction model, the prediction results contain significant uncertainty. In order to overcome this drawback, this study uses a method that integrates ensemble prediction and a stepwise regression model based on a mean-valued generation function. In addition, it utilizes empirical mode decomposition (EMD), which is a new method of handling time series. First, a non-stationary time series is decomposed into a series of intrinsic mode functions (IMFs), which are stationary and multi-scale. Then, a different prediction model is constructed for each component of the IMF using numerical ensemble prediction combined with stepwise regression analysis. Finally, the results are fit to a linear regression model, and a short-term climate prediction system is established using the Visual Studio development platform. The model is validated using temperature data from February 1957 to 2005 from 88 weather stations in Guangxi, China. The results show that compared to single-model prediction methods, the EMD and ensemble prediction model is more effective for forecasting climate change and abrupt climate shifts when using historical data for multi-step prediction.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.969-971
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• 1999

Wavelet analysis is applying to many fields such as the time-frequency localization of a time series and a time varying data. In this paper, a statistical testing based Wavelet power spectrum analysis for the stationary Nino3 Sea Surface Temperature(SST) data was executed. Specially, the 95% confidence level for SST was effective in searching the periods of El-Nino using various wavelet basis functions.