• Journal of Korea Water Resources Association
• /
• v.39 no.7 s.168
• /
• pp.617-626
• /
• 2006

In this study 3-month SPI data from 59 stations over the Korean peninsula are analyzed by deriving and spatially characterizing the EOFs. Also, the coefficient time series of EOF are applied to the multi-variate time series model to generate the time series of 10,000 years, to average them to estimate the areal average, and to decide the maximum drought severity for given return periods. Finally, the drought prevention ability is evaluated by considering the effective storage of dam within the basin and the size of agricultural area. Especially for the return period of 30 years, only the Han river basin has the potential to overcome the drought. Other river basins like the Youngsan river basin, which has a large portion of agricultural area but less water storage, are found to be very vulnerable to the rainfall-sensitive agricultural drought.

• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10a
• /
• pp.20-25
• /
• 1993

The control of the motion of a mobile robot is studied. The driving and steering motor assembly is located in the front of the mobile robot. The position of the mobile robot is determined by the steering angle and driving distance. For the controller design, a time-series multivariate model of the autogressive exogenous (ARX) type is used to describe the input-output relation. The discounted least square method is used to estimate parameters of the time-series model. A self-tuning controller is so designed that the position of the center of the mobile robot track the given trajectory. Simulation result controlled by a self-tuning controller is presented to illustrate the approach.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.5
• /
• pp.829-837
• /
• 2011

Since Harvey (1989), many approaches for applying unobserved components (UC) models to both univariate and multivariate time series analysis have been developed. However, practitioners still tend to use traditional methods such as exponential smoothing or ARIMA models for modeling and predicting time series data. It is well known that the UC model combines the flexibility of ARIMA models and the easy interpretability of exponential smoothing models by using unobserved components such as trend, cycle, season, and irregular components. This study reviews the UC model and compares its relative performances with those of the other models in modeling and predicting the real gross domestic products (GDP) in Korea. We conclude that the optimal model is the UC model on basis of root mean squared error.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.5
• /
• pp.221-229
• /
• 2021

This study aims to forecast the exchange rate of the Chinese Yuan against the US Dollar by a combination of different models as proposed by Poon and Granger (2003) during the Covid-19 pandemic. For this purpose, we include three uni-variate time series models, i.e., ARIMA, Naïve, Exponential smoothing, and one multivariate model, i.e., NARDL. This is the first of its kind endeavor to combine univariate models along with NARDL to the best of our knowledge. Utilizing monthly data from January 2011 to December 2020, we predict the Chinese Yuan against the US dollar by two combination criteria i.e. var-cor and equal weightage. After finding out the individual accuracy, the models are then assessed through equal weightage and var-cor methods. Our results suggest that Naïve outperforms all individual & combination of time series models. Similarly, the combination of NARDL and Naïve model again outperformed all of the individual as well as combined models except the Naïve model, with the lowest MAPE value of 0764. The results suggesting that the Chinese Yuan exchange rate against the US Dollar is dependent upon the recent observations of the time series. Further evidence shows that the combination of models plays a vital role in forecasting which commensurate with the literature.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.4
• /
• pp.339-347
• /
• 2021

This study aims to forecast the exchange rate by a combination of different models as proposed by Poon and Granger (2003). For this purpose, we include three univariate time series models, i.e., ARIMA, Naïve, Exponential smoothing, and one multivariate model, i.e., NARDL. This is the first of its kind endeavor to combine univariate models along with NARDL to the best of our knowledge. Utilizing monthly data from January 2011 to December 2020, we predict the Pakistani Rupee against the US dollar by a combination of different forecasting techniques. The observations from M1 2020 to M12 2020 are held back for in-sample forecasting. The models are then assessed through equal weightage and var-cor methods. Our results suggest that NARDL outperforms all individual time series models in terms of forecasting the exchange rate. Similarly, the combination of NARDL and Naïve model again outperformed all of the individual as well as combined models with the lowest MAPE value of 0.612 suggesting that the Pakistani Rupee exchange rate against the US Dollar is dependent upon the macro-economic fundamentals and recent observations of the time series. Further evidence shows that the combination of models plays a vital role in forecasting, as stated by Poon and Granger (2003).

• International Journal of Contents
• /
• v.9 no.4
• /
• pp.1-10
• /
• 2013

The electroencephalogram (EEG) time series is a measure of electrical activity received from multiple electrodes placed on the scalp of a human brain. It provides a direct measurement for characterizing the dynamic aspects of brain activities. These EEG signals are formed from a series of spatial and temporal data with multiple dimensions. Missing data could occur due to fault electrodes. These missing data can cause distortion, repudiation, and further, reduce the effectiveness of analyzing algorithms. Current methodologies for EEG analysis require a complete set of EEG data matrix as input. Therefore, an accurate and reliable imputation approach for missing values is necessary to avoid incomplete data sets for analyses and further improve the usage of performance techniques. This research proposes a new method to automatically recover random consecutive missing data from real world EEG data based on Linear Dynamical System. The proposed method aims to capture the optimal patterns based on two main characteristics in the coevolving EEG time series: namely, (i) dynamics via discovering temporal evolving behaviors, and (ii) correlations by identifying the relationships between multiple brain signals. From these exploits, the proposed method successfully identifies a few hidden variables and discovers their dynamics to impute missing values. The proposed method offers a robust and scalable approach with linear computation time over the size of sequences. A comparative study has been performed to assess the effectiveness of the proposed method against interpolation and missing values via Singular Value Decomposition (MSVD). The experimental simulations demonstrate that the proposed method provides better reconstruction performance up to 49% and 67% improvements over MSVD and interpolation approaches, respectively.

• Journal of Korea Port Economic Association
• /
• v.35 no.3
• /
• pp.61-72
• /
• 2019

In order to enhance the competitiveness of the container shipping industry and promote its development, based on the empirical analyses using multivariate time series models, this study aims to suggest a few strategies related to the dynamics of the container shipping market. It uses the vector autoregressive (VAR) and vector error correction (VEC) models as analytical methodologies. Additionally, it uses the annual trade volumes, fleets, and freight rates as the dataset. According to the empirical results, we can infer that the most exogenous variable, the trade volume, exerted the highest influence on the total dynamics of the container shipping market. Based on these empirical results, this study suggests some implications for ship investment, freight rate forecasting, and the strategies of shipping firms. Concerning ship investment, since the exogenous trade volume variable contributes most to the uncertainty of freight rates, corporate finance can be considered more appropriate for container ship investment than project finance. Concerning the freight rate forecasting, the VAR and VEC models use the past information and the cointegrating regression model assumes future information, and hence the former models are found better than the latter model. Finally, concerning the strategies of shipping firms, this study recommends the use of cycle-linked repayment scheme and services contract.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 1999.06a
• /
• pp.43-48
• /
• 1999

To clarify the motion characteristics of free-style Okinawan dance“Kachaasi”, first the subjective impression was quantitatively evaluated with semantic differential technique to cluster its types. Then, the contingency of joint rotation in shoulder, elbow and wrist joints was examined with multivariate autoregressive model. The time-series data of positions and angels of three joints were calculated according to the deforming conditions and shielding directions of the ring lights. As the results, in an excellent dancer, the motions of shoulder and elbow were highly synchronized and smoothly controlled. The low-frequency output of the shoulder and elbow were mutually interacted. Meanwhile, the wrist behaved independently of other joints' rotation.

• Smart Media Journal
• /
• v.13 no.7
• /
• pp.9-17
• /
• 2024

Due to the increase in energy consumption, and eco-friendly policies, there is a need for efficient energy consumption in buildings. Anomaly power detection based on deep learning are being used. Because of the difficulty in collecting anomaly data, anomaly detection is performed using reconstruction error with a Recurrent Neural Network(RNN) based autoencoder. However, there are some limitations such as the long time required to fully learn temporal features and its sensitivity to noise in the train data. To overcome these limitations, this paper proposes the TCN-USAD, combined with Temporal Convolution Network(TCN) and UnSupervised Anomaly Detection for multivariate data(USAD). The proposed model using TCN-based autoencoder and the USAD structure, which uses two decoders and adversarial training, to quickly learn temporal features and enable robust anomaly detection. To validate the performance of TCN-USAD, comparative experiments were performed using two building energy datasets. The results showed that the TCN-based autoencoder can perform faster and better reconstruction than RNN-based autoencoder. Furthermore, TCN-USAD achieved 20% improved F1-Score over other anomaly detection models, demonstrating excellent anomaly detection performance.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.4
• /
• pp.11-19
• /
• 2021

This study aims to analyze the causality relationship between sectoral stock markets (agricultural, financial, industrial, and mining sectors) and economic growth in the short and long term as well as to analyze whether it has similar types or not. The data used is quarterly time-series data (first quarter 2009 to fourth 2019). To determine the causality relationship, this study conducts a variable and multivariate causality test. The results of the varying granger causality test show that there is only a one-way relationship, where the economic growth of the agriculture sector affects its shares. A one-way relationship also occurs in stocks of the industrial sector, which has an influence on economic growth. The multivariate causality test shows that the economic growth of the agricultural sector has a two-way causality relationship, and it also exists between the industrial sector and the financial sector stock markets. The two-way causality relationship between the stock market and sectoral economic growth is a convergence towards long-term equilibrium. The findings of this study suggest that the government through the Financial Services Authority and the Indonesia Stock Exchange have to maintain stability in the stock market as a supporter of the national economy.