• Communications for Statistical Applications and Methods
• /
• 제22권3호
• /
• pp.209-222
• /
• 2015

Time series are used in many studies for model building and analysis. We must be very careful to understand the kind of time series data used in the analysis. In this review article, we will begin with some issues related to the use of aggregate and systematic sampling time series. Since several time series are often used in a study of the relationship of variables, we will also consider vector time series modeling and analysis. Although the basic procedures of model building between univariate time series and vector time series are the same, there are some important phenomena which are unique to vector time series. Therefore, we will also discuss some issues related to vector time models. Understanding these issues is important when we use time series data in modeling and analysis, regardless of whether it is a univariate or multivariate time series.

• 대한산업공학회지
• /
• 제25권1호
• /
• pp.111-124
• /
• 1999

This evapaper is toluate the forecasting performance of three neural network(NN) approaches against ARIMA model using the famous time series analysis competition data. The first NN approach is to analyze the second Makridakis (M2) Competition Data using Multilayer Perceptron (MLP) that has been the most popular NN model in time series analysis. Since it is recently known that MLP suffers from bias/variance dilemma, two approaches are suggested in this study. The second approach adopts Cascade Correlation Network (CCN) that was suggested by Fahlman & Lebiere as an alternative to MLP. In the third approach, a time series is separated into two series using Noise Filtering Network (NFN) that utilizes autoassociative memory function of neural network. The forecasts in the decomposition analysis are the sum of two prediction values obtained from modeling each decomposed series, respectively. Among the three NN approaches, Decomposition Analysis shows the best forecasting performance on the M2 Competition Data, and is expected to be a promising tool in analyzing socio-economic time series data because it reduces the effect of noise or outliers that is an impediment to modeling the time series generating process.

• International Journal of Computer Science & Network Security
• /
• 제22권2호
• /
• pp.185-192
• /
• 2022

Forecasting and time series modelling plays a vital role in the data analysis process. Time Series is widely used in analytics & data science. Forecasting stock prices is a popular and important topic in financial and academic studies. A stock market is an unregulated place for forecasting due to the absence of essential rules for estimating or predicting a stock price in the stock market. Therefore, predicting stock prices is a time-series problem and challenging. Machine learning has many methods and applications instrumental in implementing stock price forecasting, such as technical analysis, fundamental analysis, time series analysis, statistical analysis. This paper will discuss implementing the stock price, forecasting, and research using prophet and LSTM models. This process and task are very complex and involve uncertainty. Although the stock price never is predicted due to its ambiguous field, this paper aims to apply the concept of forecasting and data analysis to predict stocks.

• East Asian mathematical journal
• /
• 제26권1호
• /
• pp.105-113
• /
• 2010

In the spectral analysis, Fourier coeffcients are very important to give informations for the original signal f on a finite domain, because they recover f. Also Fourier analysis has extension to wavelet analysis for the whole space R. Various kinds of reconstruction theorems are main subject to analyze signal function f in the field of wavelet analysis. In this paper, we will present a new reconstruction theorem of functions in $L^1(R)$ using a nonharmonic Fourier series. When we construct this series, we have used dyadic subdivision schemes.

• 한국컴퓨터정보학회논문지
• /
• 제20권2호
• /
• pp.113-120
• /
• 2015

Technology analysis is to analyze technological data such as patent and paper for a given technology field. From the results of technology analysis, we can get novel knowledge for R&D planing and management. For the technology analysis, we can use diverse methods of statistics. Time series analysis is one of efficient approaches for technology analysis, because most technologies have researched and developed depended on time. So many technological data are time series. Time series data are occurred through time. In this paper, we propose a methodology of technology forecasting using the dynamic time warping (DTW) of time series analysis. To illustrate how to apply our methodology to real problem, we perform a case study of patent documents in target technology field. This research will contribute to R&D planning and technology management.

• 한국지하수토양환경학회:학술대회논문집
• /
• 한국지하수토양환경학회 2003년도 추계학술발표회
• /
• pp.254-257
• /
• 2003

Statistical analysis is performed to estimate the correlations between geological or geographical factor and groundwater inflow rates in the Seoul subway system. Correlation analysis shows that among several geological and geographical factors fractures and streams have most strong effects on inflow rate into tunnels. In particular, subway line 5∼8 are affected more by these factors than subway line 1∼4. Time series analysis is carried out to forecast groundwater inflow rate. Time series analysis is a useful empirical method for simulation and forecasts in case that physical model can not be applied to. The time series of groundwater inflow rates is calculated using the observation data. Transfer function-noise model is applied with the precipitation data as input variables. For time series analysis, statistical methods are performed to identify proper model and autoregressive-moving average models are applied to evaluation of inflow rate. Each model is identified to satisfy the lowest value of information criteria. Results show that the values by result equations are well fitted with the actual inflow rate values. The selected models could give a good explanation of inflow rates variation into subway tunnels.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2005년도 학술발표회 논문집
• /
• pp.792-796
• /
• 2005

The methods of times series analysis have been recognized as important tools for assisting in solving problems related to the management of water resources. Especially, After more than 40 years the so-called Hurst effect remains an open problem in stochastic hydrology. Until now, its existence has been explained fly R/S analysis that roots in early work of the British hydrologist H.E. Hurst(1951). Today, the Hurst analysis is mostly used for the hydrological studies for memory and characteristics of time series and many methodologies have been developed for the analysis. So, there are many different techniques for the estimation of the Hurst exponent(H). However, the techniques can produce different characteristics for the persistence of a time series each other. We found that DFA is the most appropriate technique for the Hurst exponent estimation for both the shot term memory and long term memory. We analyze the SOI(Southern Oscillations Index) and 6 tree-ring series for USA sites by means of DFA and the BDS statistic is used for nonlinearity test of the series. From the results, we found that SOI series is nonlinear time series which has a long term memory of H=0.92. Contrary to earlier work of Rao(1999), all the tree- ring series are not random from our analysis. A certain tree ring series show a long term memory of H=0.97 and nonlinear property. Therefore, we can say that the SOI and tree-ring series may show long memory and nonlinearity.

• 한국토양비료학회지
• /
• 제48권4호
• /
• pp.255-261
• /
• 2015

Soil catena can be characterized by some properties, such as drainage levels and soil textures. Characteristics of soil catena are different drainage levels from a summit to the direction of gravity and similar soil textures. Therefore this study was performed GIS (Geographic information system) and statistical analyses using perimeters from soil series in order to characterize quantitatively and objectively soil distributional properties in Korea. The total of 16 soil series from representative granite and granite gneiss originated soils were selected among inland soils from detailed soil maps (1:25,000 scale) in Rural Development Administration (RDA) and analyzed. After the detailed soil maps were merged by soil series unit, perimeters were measured from one soil series to neighboring soil series using functions of table join, merge, dissolve, buffer, and clip in ArcGIS (10.1). The covering ratio of each soil series unit was calculated from neighboring perimeters by soil series and applied to clustering analysis. Soils that were analyzed were the total of 16 soil series; 7 of sandy loam and 9 of clay loam. As a result, analyzed soil series adjoined complicatedly such as Hyocheon series adjoined 26 series and Jisan did 276 series. The results of the clustering analysis showed that soils were clustered by soil textures except a few soil series. This study applied only one property that was a length of neighboring soil series to GIS and statistical analyses. These results were compared to existing soil groups that were classified by new-soil taxonomy, texture, soil type and drainage level. It showed that these analyses can provide soil characteristics by soil texture. Based on this study, there is a need to investigate further objectively and quantitatively in statistical analyses of soil series.

• Communications for Statistical Applications and Methods
• /
• 제17권3호
• /
• pp.377-390
• /
• 2010

In the case of time-series analysis, it is often more convenient to rely on the frequency domain than the time domain. Spectral density is the core of the frequency-domain analysis that describes autocorrelation structures in a time-series process. Possible ways to estimate spectral density are to compute a periodogram or to average the periodogram over some frequencies with (un)equal weights. This can be an attractive tool to measure the similarity between time-series processes. We employ the metrics based on a smoothed periodogram proposed by Park and Kim (2008) for the classification of different classes of time-series processes. We consider several lag windows with unequal weights instead of a modified Daniel's window used in Park and Kim (2008). We evaluate the performance under various simulation scenarios. Simulation results reveal that the metrics used in this study split the time series into the preassigned clusters better than do the raw-periodogram based ones proposed by Caiado et al. 2006. Our metrics are applied to an economic time-series dataset.

• 환경생물
• /
• 제34권4호
• /
• pp.365-373
• /
• 2016

환경생태 자료 분석에 사용된 많은 자료가 시간에 따라 얻어지고 있다. 조사된 시점의 수가 적은 경우에는 자료가 충분한 정보를 주지 않기 때문에 반복 측정하거나 여러 지점을 조사하여 종합적인 분석을 수행하게 된다. 이때 사용하는 방법이 경시적 자료 분석(longitudinal data analysis) 또는 혼합모형(mixed model) 분석이다. 그러나 시점의 수가 많아 정보의 양이 충분하다면 반복적인 자료가 필요하지 않으며 이러한 자료는 시계열 분석 기법을 이용하여 분석하게 된다. 특히 현재와 같이 다수의 시점에서 얻어진 자료의 수가 많아지고 있는 상항에서 각 변수 간에 서로 어떤 영향을 주는지 또는 향후 어떤 경향을 띠게 되는지 예측을 원한다면 시계열 분석 기법을 사용하여 자료를 분석해야 한다. 본 연구에서는 단변량 시계열 분석(univariate time series analysis), 개입 분석(intervention time series model), 전이함수 모형 분석(transfer function model), 다변량 시계열 분석(multivariate time series model) 기법을 소개하고 현재까지 진행된 국내외 연구 논문을 살펴보았다. 또한 향후 환경생태 자료 분석에서 중요하게 사용될 수 있는 오차수정 모형(error correction model)을 소개하였다.