• Title, Summary, Keyword: Time Series Analysis

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Issues Related to the Use of Time Series in Model Building and Analysis: Review Article

  • Wei, William W.S.
    • Communications for Statistical Applications and Methods
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    • v.22 no.3
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    • pp.209-222
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    • 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.

Decomposition Analysis of Time Series Using Neural Networks (신경망을 이용한 시계열의 분해분석)

  • Jhee, Won-Chul
    • Journal of Korean Institute of Industrial Engineers
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    • v.25 no.1
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    • pp.111-124
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    • 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.

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A Technology Analysis Model using Dynamic Time Warping

  • Choi, JunHyeog;Jun, SungHae
    • Journal of the Korea Society of Computer and Information
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    • v.20 no.2
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    • pp.113-120
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    • 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.

Bayes Inference for the Spatial Bilinear Time Series Model with Application to Epidemic Data

  • Lee, Sung-Duck;Kim, Duk-Ki
    • The Korean Journal of Applied Statistics
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    • v.25 no.4
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    • pp.641-650
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    • 2012
  • Spatial time series data can be viewed as a set of time series simultaneously collected at a number of spatial locations. This paper studies Bayesian inferences in a spatial time bilinear model with a Gibbs sampling algorithm to overcome problems in the numerical analysis techniques of a spatial time series model. For illustration, the data set of mumps cases reported from the Korea Center for Disease Control and Prevention monthly over the years 2001~2009 are selected for analysis.

Classification of Time-Series Data Based on Several Lag Windows

  • Kim, Hee-Young;Park, Man-Sik
    • Communications for Statistical Applications and Methods
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    • v.17 no.3
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    • pp.377-390
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    • 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.

Correlation analysis and time series analysis of Ground-water inflow rate into tunnel of Seoul subway system

  • 김성준;이강근;염병우
    • Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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    • pp.254-257
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    • 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.

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Time Series Analysis and Forecasting of Electrical Conductivity in Coastal Aquifers (연안암반대수층의 해수침투경향성 파악을 위한 전기전도도 시계열 분석과 예측)

  • Ju, Jeong-Woung;Yeo, In Wook
    • Economic and Environmental Geology
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    • v.50 no.4
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    • pp.267-276
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    • 2017
  • Seawater intrusion into coastal fractured rock aquifer, resulting in groundwater contamination, is of serious concern in coastal areas of Jeolla Namdo, Korea, which heavily depends on groundwater resources. Time series analysis and forecasting were carried out to analyze and predict EC which is a major indicator of seawater intrusion. Two time series models of autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) were tested for suggesting appropriate time series model. Time series data of EC measured over one year showed a increasing trend with short periodic fluctuations, due to tidal effect and pumping, which indicated that EC time series data tended to be non-stationary. SARIMA model was found better fitted to observed EC than any other time series model. Time series analysis and modeling was found to be a useful tool to analyze EC at coastal fractured rock aquifer subject to seawater intrusion.

A Study on Time-series Clustering Analysis based on Dynamic Time Warping (동적 시간워핑을 활용한 시계열자료의 군집분석)

  • Kim, Seong Tae;Park, Man Sik
    • Journal of the Korean Data Analysis Society
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    • v.20 no.5
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    • pp.2319-2332
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    • 2018
  • Two different approaches are considered for the clustering analysis of time-series data: time-domain approach and frequency-domain one. In the time domain, distance metrics measuring similarities among the time-series data take the estimation results under certain parametric models or autocorrelation structures inherent in each of the processes into account. The frequency-domain approach also plays an important role in time-series clustering analysis by transforming auto-covariance function into spectrum prior to measuring similarities among the processes. However, the previous time-series clustering approaches depend on assumptions of distribution or models. In this study, we apply the dynamic time warping (DTW) algorithm in which no assumptions are needed. This algorithm enables us to compare two time-series processes in order to measure similarities even when one process is temporally shifted from the other one. We evaluate the performance of DTW and compare with the metrics forementioned via the simulation study. For the real application, we considered the U.S. state-level seasonally adjusted monthly unemployment rate data.

A Review of Time Series Analysis for Environmental and Ecological Data (환경생태 자료 분석을 위한 시계열 분석 방법 연구)

  • Mo, Hyoung-ho;Cho, Kijong;Shin, Key-Il
    • Korean Journal of Environmental Biology
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    • v.34 no.4
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    • pp.365-373
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    • 2016
  • Much of the data used in the analysis of environmental ecological data is being obtained over time. If the number of time points is small, the data will not be given enough information, so repeated measurements or multiple survey points data should be used to perform a comprehensive analysis. The method used for that case is longitudinal data analysis or mixed model analysis. However, if the amount of information is sufficient due to the large number of time points, repetitive data are not needed and these data are analyzed using time series analysis technique. In particular, with a large number of data points in the current situation, when we want to predict how each variable affects each other, or what trends will be expected in the future, we should analyze the data using time series analysis techniques. In this study, we introduce univariate time series analysis, intervention time series model, transfer function model, and multivariate time series model and review research papers studied in Korea. We also introduce an error correction model, which can be used to analyze environmental ecological data.

The usefulness of overfitting via artificial neural networks for non-stationary time series

  • Ahn Jae-Joon;Oh Kyong-Joo;Kim Tae-Yoon
    • Proceedings of the Korean Operations and Management Science Society Conference
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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.

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