• Environmental and Resource Economics Review
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• v.12 no.4
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• pp.687-708
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• 2003

The purpose of this paper is to introduce time-series causality analysis by combining time-series technique with graph theory. Vector autoregressive (VAR) models can provide reasonable interpretation only when the contemporaneous variables stand in a well-defined causal order. We show that how graph theory can be applied to search for the causal structure In VAR analysis. Using Maryland crop cash prices and CBOT futures price data, we estimate a VAR model with directed acyclic graph analysis. This expands our understanding the degree of interconnectivity between the employed time-series variables.

• Journal of Korea Multimedia Society
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• v.24 no.9
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• pp.1251-1260
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• 2021

This paper discusses missing data processing using simple moving average (SMA) and kalman filter. Also SMA and kalman predictive value are made a comparative study. Time series analysis is a generally method to deals with time series data in photovoltaic field. Photovoltaic system records data irregularly whenever the power value changes. Irregularly recorded data must be transferred into a consistent format to get accurate results. Missing data results from the process having same intervals. For the reason, it was imputed using SMA and kalman filter. The kalman filter has better performance to observed data than SMA. SMA graph is stepped line graph and kalman filter graph is a smoothing line graph. MAPE of SMA prediction is 0.00737%, MAPE of kalman prediction is 0.00078%. But time complexity of SMA is O(N) and time complexity of kalman filter is O(D²) about D-dimensional object. Accordingly we suggest that you pick the best way considering computational power.

• International Journal of Contents
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• v.17 no.3
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• pp.1-14
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• 2021

Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, a lot of efforts have been made in the field of data science to help combat against this disease. Among them, forecasting the number of cases of infection is a crucial problem to predict the development of the pandemic. Many deep learning-based models can be applied to solve this type of time series problem. In this research, we would like to take a step forward to incorporate spatial data (geography) with time series data to forecast the cases of region-level infection simultaneously. Specifically, we model a single spatio-temporal graph, in which nodes represent the geographic regions, spatial edges represent the distance between each pair of regions, and temporal edges indicate the node features through time. We evaluate this approach in COVID-19 in a Korean dataset, and we show a decrease of approximately 10% in both RMSE and MAE, and a significant boost to the training speed compared to the baseline models. Moreover, the training efficiency allows this approach to be extended for a large-scale spatio-temporal dataset.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.111-117
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• 2023

There is a lot of research on using a combination of graph neural networks and recurrent neural networks as a way to account for both temporal and spatial dependencies. In particular, graph neural networks are an emerging area of research. Seoul's bicycle rental service (aka Daereungi) has rental stations all over the city of Seoul, and the rental information at each station is a time series that is faithfully recorded. The rental information of each rental station has temporal characteristics that show periodicity over time, and regional characteristics are also thought to have important effects on the rental status. Regional correlations can be well understood using graph neural networks. In this study, we reconstructed the time series data of Seoul's bicycle rental service into a graph and developed a rental prediction model that combines a graph neural network and a recurrent neural network. We considered temporal characteristics such as periodicity over time, regional characteristics, and the degree importance of each rental station.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.149-156
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• 2021

Gambling negatively affects the economy, and it brings unwanted financial, social, and health outcomes to gamblers. On the one hand, unemployment is argued to be a leading cause of gambling. On the other hand, gambling can cause unemployment in the second-order via gambling-induced poor health, falling productivity, and crime. In terms of significant effects, previous studies were able to establish an association, but not causality. The current study examines the time-sequence and contemporaneous causalities between lottery gambling and unemployment in Thailand. The Granger causality and directed acyclic graph (DAG) tests employ time-series data on gambling- and unemployment-related Google Trends indexes from January 2004 to April 2021 (208 monthly observations). These tests are based on the estimates from a vector autoregressive (VAR) model. Granger causality is a way to investigate causality between two variables in a time series. However, this approach cannot detect the contemporaneous causality among variables that occurred within the same period. The contemporaneous causal structure of gambling and unemployment was identified via the data-determined DAG approach. The use of time-series Google Trends indexes in gambling studies is new. Based on this data set, unemployment is found to contemporaneously cause gambling, whereas gambling Granger causes unemployment. The causalities are circular and last for four months.

• The KIPS Transactions:PartC
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• v.15C no.2
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• pp.93-102
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• 2008

The network weather map and bandwidth time-series graph are popularly used to understand the current and past traffic condition of NSP, ISP, and enterprise networks. These systems collect traffic performance data from a SNMP agent running on the network devices such as routers and switches, store the gathered information into a DB, and display the network performance status in the form of a time-series graph or a network weather map using Web user interface. Most of current enterprise networks are constructed in the form of a hierarchical tree-like structure with multi-Gbps Ethernet links, which is quietly different from the national or world-wide backbone network structure. This paper focuses on the network weather map for current enterprise network. We start with the considering points in developing a network weather map system suitable for enterprise network. Based on these considerings, this paper proposes the best way of using SNMP in constructing a network weather map system. To prove our idea, we designed and developed a network weather map system for our campus network, which is also described in detail.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.193-202
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• 2021

The update of a high-precision map was carried out by modifying the geometric information using ortho-images or point-cloud data as the source data and then reconstructing the relationship between the spatial objects. These series of processes take considerable time to process the geometric information, making it difficult to apply real-time route planning to a vehicle quickly. Therefore, this study proposed a method to update the road network for route planning using a graph data structure and storage type of graph data structure considering the characteristics of the road network. The proposed method was also reviewed to assess the feasibility of real-time route information transmission by applying it to actual road data.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.37-40
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• 2024

본 연구에서는 웨어러블 디바이스로부터 수집된 다변량 반려동물 행동 데이터를 처리하기 위해, GCN(Graph Convolutional Network)과 GRU(Gated Recurrent Unit)를 결합한 모델을 제안한다. 제안된 모델은 시계열 내부 구조를 활용하여 그래프 구조로 변환하고, DTW(Dynamic Time Warping) 유사도 분석을 통해 노드 간의 시간적 유사도를 기반으로 엣지를 생성한다. 실험결과로 DTW 기반 엣지 생성 방식이 유클리드 거리 및 선형 방식에 비해 더 높은 성능을 나타냈다. 본 연구는 반려동물의 행동을 정확히 분류하기 위한 효과적인 방법론을 제공한다.

• Korean Management Science Review
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• v.28 no.1
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• pp.53-60
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• 2011

Time series analysis methods have been traditionally used in stock price prediction. However, most of the existing methods represent some methodological limitations in reflecting influence from external factors that affect the fluctuation of stock prices, such as oil prices, exchange rates, money interest rates, and the stock price indexes of other countries. To overcome the limitations, we propose a network based method incorporating the relations between the individual company stock prices and the external factors by using a graph-based semi-supervised learning algorithm. For verifying the significance of the proposed method, it was applied to the prediction problems of company stock prices listed in the KOSPI from January 2007 to August 2008.

• Journal of Biomedical Engineering Research
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• v.12 no.4
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• pp.295-302
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• 1991

In this paper, the inter- and intra-frame distortions in the gray levels of a series of fluorescein ocular fundus photographs are corrected. For doing this, the background images are extracted from original images using the image blurring effect by decimation, and then shading corrected images are obtained by subtracting the background images from the original images pixel by pixel. In a series of fluorescein ocular fundus photographs, after the gray scale distoriton is corrected, the intensity volumes of dye leakage are measured and represented by a graph. These data may be useful for the prediction of prognosis and the therapeutic management.