• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.374-377
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• 2005

The distribution and changes of tropospheric nitrogen dioxide ($NO_2$) are analyzed using the satellite measurements data from GOME (Global Ozone Monitoring Experiment) and SCIMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY). We produced global maps of tropospheric $NO_2$ for 4 seasons using GOME measurements from January 1997 to June 2003. The global distribution shows high values in regions with dense population and high industrialization. Tropospheric $NO_2$ shows obvious seasonal changes depending on its emission and lifetime. Based on the good agreement between two instruments in the time period of overlapping measurements (January 2003-June2003), we linked SClAMACHY data to the GOME time series. The combined time series over the past decade indicate that $NO_2$ 1evels over China are rapidly increasing while those over Europe are decreasing. We also discussed potential application of spaceborne instruments in detecting and characterizing long-distance transport of $NO_2$.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.2
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• pp.1-11
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• 2023

This study analyzed the impact of greenhouse cultivation area and groundwater level changes due to the water curtain cultivation in the greenhouse complexes. The groundwater observation data in the Miryang study area were used and classified into greenhouse and field cultivation areas to compare the groundwater impact of water curtain cultivation in the greenhouse complex. We identified the characteristics of the groundwater time series data by the terrain of the study area and selected the optimal model through time series analysis. We analyzed the time series data for each terrain's two representative groundwater observation wells. The Seasonal ARIMA model was chosen as the optimal model for riverside well, and for plain and mountain well, the ARIMA model and Seasonal ARIMA model were selected as the optimal model. A suitable prediction model is not limited to one model due to a change in a groundwater level fluctuation pattern caused by a surrounding environment change but may change over time. Therefore, it is necessary to periodically check and revise the optimal model rather than continuously applying one selected ARIMA model. Groundwater forecasting results through time series analysis can be used for sustainable groundwater resource management.

• Communications for Statistical Applications and Methods
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• v.30 no.4
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• pp.369-388
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• 2023

In this paper, we develop the two-step procedure that detects and estimates the position of structural changes for multivariate nonstationary time series, either on mean parameters or second-order structures. We first investigate the presence of mean structural change by monitoring data through the aggregated cumulative sum (CUSUM) type statistic, a sequential procedure identifying the likely position of the change point on its trend. If no mean change point is detected, the proposed method proceeds to scan the second-order structural change by modeling the multivariate nonstationary time series with a multivariate locally stationary Wavelet process, allowing the time-localized auto-correlation and cross-dependence. Under this framework, the estimated dynamic spectral matrices derived from the local wavelet periodogram capture the time-evolving scale-specific auto- and cross-dependence features of data. We then monitor the change point from the lower-dimensional approximated space of the spectral matrices over time by applying the dynamic principal component analysis. Different from existing methods requiring prior information on the type of changes between mean and covariance structures as an input for the implementation, the proposed algorithm provides the output indicating the type of change and the estimated location of its occurrence. The performance of the proposed method is demonstrated in simulations and the analysis of two real finance datasets.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.694-706
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• 2019

In this study, we applied the long short-term memory (LSTM) model to classify the cryptocurrency price time series. We collected historic cryptocurrency price time series data and preprocessed them in order to make them clean for use as train and target data. After such preprocessing, the price time series data were systematically encoded into the three-dimensional price tensor representing the past price changes of cryptocurrencies. We also presented our LSTM model structure as well as how to use such price tensor as input data of the LSTM model. In particular, a grid search-based k-fold cross-validation technique was applied to find the most suitable LSTM model parameters. Lastly, through the comparison of the f1-score values, our study showed that the LSTM model outperforms the gradient boosting model, a general machine learning model known to have relatively good prediction performance, for the time series classification of the cryptocurrency price trend. With the LSTM model, we got a performance improvement of about 7% compared to using the GB model.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.1-6
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• 2011

As large-scale building projects have recently increased for the residential, commercial and office facilities, construction costs for these projects have become a matter of great concern, due to their significant construction cost implications, as well as unpredictable market conditions and fluctuations in the rate of inflation during the projects' long-term construction periods. In particular, recent volatile fluctuations of construction material prices fueled such problems as cost forecasting. This research develops a time series model using the Box-Jenkins approach and material price time series data in Korea in order to forecast trends in the unit prices of required materials. Building information modeling (BIM) approaches are also used to analyze injection times of construction resources and to conduct quantity take-off so that total material prices can be forecast. To determine an optimal time series model for forecasting price trends, comparative analysis of predictability of tentative autoregressive integrated moving average (ARIMA) models is conducted. The proposed BIM-based time series forecasting model can help to deal with sudden changes in economic conditions by estimating material prices that correspond to resource injection times.

• Journal of Environmental Health Sciences
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• v.50 no.2
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• pp.125-137
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• 2024

Background: The Korea Risk Information Surveillance System (K-RISS) was developed to enable the early detection of food and drug safety-related issues. Its goal is to deliver real-time risk indicators generated from ongoing food and drug risk monitoring. However, the existing K-RISS system suffers under several limitations. Objectives: This study aims to augment K-RISS with more detailed indicators and establish a severity standard that takes into account structural changes in the daily time series of K-RISS values. Methods: First, a Delphi survey was conducted to derive the required weights. Second, a control chart, commonly used in statistical process controls, was utilized to detect outliers and establish caution, attention, and serious levels for K-RISS values. Furthermore, Bai and Perron's method was employed to determine structural changes in K-RISS time series. Results: The study incorporated 'closeness to life' and 'sustainability' indicators into K-RISS. It obtained the necessary weights through a survey of experts for integrating variables, combining indicators by data source, and aggregating sub K-RISS values. We defined caution, attention, and serious levels for both average and maximum values of daily K-RISS. Furthermore, when structural changes were detected, leading to significant variations in daily K-RISS values according to different periods, the study systematically verified these changes and derived respective severity levels for each period. Conclusions: This study enhances the existing K-RISS system and introduces more advanced indicators. K-RISS is now more comprehensively equipped to serve as a risk warning index. The study has paved the way for an objective determination of whether the food safety risk index surpasses predefined thresholds through the application of severity levels.

• KIISE Transactions on Computing Practices
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• v.22 no.5
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• pp.201-208
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• 2016

Collection and storing of multiple time series data in real time requires large memory space. To solve this problem, the usage of varying sample size is proposed in the compression scheme using discrete cosine transform technique. Time series data set has characteristics such that a higher compression ratio can be achieved with smaller amount of value changes and lower frequency of the value changes. The coefficient of variation and the variability of the differences between adjacent data elements (VDAD) are presumed to be very good measures to represent the characteristics of the time series data and used as key parameters to determine the varying sample size. Test results showed that both VDAD-based and the coefficient of variation-based scheme generate excellent compression ratios. However, the former scheme uses much simpler sample size decision mechanism and results in better compression performance than the latter scheme.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.6
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• pp.137-143
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• 2010

A method for improving prediction accuracy through processing time series data has been studied in this research. We have designed techniques to model multiple similar time series data and avoided the shortcomings of single prediction model. We predicted the future changes by effective rules derived from these models. The methods for testing prediction accuracy consists of three types: fixed interval, sliding, and cumulative method. Among the three, cumulative method produced the highest accuracy.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.31-36
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• 2008

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the enviromuent. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.920-924
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• 2005

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the environment. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.