• Title, Summary, Keyword: Seasonal dynamic

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SEASONAL AND INTER-ANNUAL VARIATION OF SEA SURFACE CURRENT IN THE GULF OF THAILAND

  • Sojisuporn, Pramot;Morimoto, Akihiko;Yanagi, Tetsuo
    • Proceedings of the KSRS Conference
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    • v.1
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    • pp.352-355
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    • 2006
  • In this study, the seasonal and inter-annual variation of sea surface current in the Gulf of Thailand were revealed through the use of WOD temperature and salinity data and monthly sea surface dynamic heights (SSDH) from TOPEX/Poseidon and ERS-2 altimetry data during 1995-2001. The mean dynamic height and mean geostrohic current were derived from the climatological data while SSDH data gave monthly dynamic heights and their geopstrophic currents. The mean geostrophic current showed strong southward and westward flow of South China Sea water along the gulf entrance. Counterclockwise eddy in the inner gulf and the western side of the gulf entrance associated with upwelling in the area. Seasonal geostrophic currents show basin-wide counterclockwise circulation during the southwest monsoon season and clockwise circulation during the northeast monsoon season. Upwelling was enhanced during the southwest monsoon season. The circulation patterns varied seasonally and inter-annually probably due to the variation in wind regime. And finally we found that congregation, spawning, and migration routes of short-bodied mackerel conform well with coastal upwelling and surface circulation in the gulf.

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Long-Term Forecasting by Wavelet-Based Filter Bank Selections and Its Application

  • Lee, Jeong-Ran;Lee, You-Lim;Oh, Hee-Seok
    • The Korean Journal of Applied Statistics
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    • v.23 no.2
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    • pp.249-261
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    • 2010
  • Long-term forecasting of seasonal time series is critical in many applications such as planning business strategies and resolving possible problems of a business company. Unlike the traditional approach that depends solely on dynamic models, Li and Hinich (2002) introduced a combination of stochastic dynamic modeling with filter bank approach for forecasting seasonal patterns using highly coherent(High-C) waveforms. We modify the filter selection and forecasting procedure on wavelet domain to be more feasible and compare the resulting predictor with one that obtained from the wavelet variance estimation method. An improvement over other seasonal pattern extraction and forecasting methods based on such as wavelet scalogram, Holt-Winters, and seasonal autoregressive integrated moving average(SARIMA) is shown in terms of the prediction error. The performance of the proposed method is illustrated by a simulation study and an application to the real stock price data.

A Study on Dynamic Change of Transportation Demand Using Seasonal ARIMA Model (계절성을 감안한 ARIMA 모형을 이용한 교통수요 동태적 변화 연구)

  • Lee, Jae-Min;Gwon, Yong-Jae
    • Journal of Korean Society of Transportation
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    • v.29 no.5
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    • pp.139-155
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    • 2011
  • This study is to estimate the dynamic change of the regional railway passenger traffic and, based on the estimated, to forecast the future regional railway passenger traffic by using the Seasonal ARIMA model. The existing studies using ARIMA failed to consider seasonality nor the monthly or the quarterly data. It was attempted in this study to use the monthly regional railway passenger traffic data to propose a model that estimates dynamic change of demand. The authors employed the Seasonal ARIMA model previously developed and used (1) the numbers of monthly passenger data and (2) the monthly passenger-km data. The test results showed that the numbers of passengers in 2015 and 2020 would increase by 36% and 71%, respectively, compared to those in 2008. The numbers of passenger-kms in 2015 and 2020 would increase by 25% and 78%, respectively, compared to those in 2008.

A study on electricity demand forecasting based on time series clustering in smart grid (스마트 그리드에서의 시계열 군집분석을 통한 전력수요 예측 연구)

  • Sohn, Hueng-Goo;Jung, Sang-Wook;Kim, Sahm
    • The Korean Journal of Applied Statistics
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    • v.29 no.1
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    • pp.193-203
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    • 2016
  • This paper forecasts electricity demand as a critical element of a demand management system in Smart Grid environment. We present a prediction method of using a combination of predictive values by time series clustering. Periodogram-based normalized clustering, predictive analysis clustering and dynamic time warping (DTW) clustering are proposed for time series clustering methods. Double Seasonal Holt-Winters (DSHW), Trigonometric, Box-Cox transform, ARMA errors, Trend and Seasonal components (TBATS), Fractional ARIMA (FARIMA) are used for demand forecasting based on clustering. Results show that the time series clustering method provides a better performances than the method using total amount of electricity demand in terms of the Mean Absolute Percentage Error (MAPE).

Fault Detection in the Semiconductor Etch Process Using the Seasonal Autoregressive Integrated Moving Average Modeling

  • Arshad, Muhammad Zeeshan;Nawaz, Javeria Muhammad;Hong, Sang Jeen
    • Journal of Information Processing Systems
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    • v.10 no.3
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    • pp.429-442
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    • 2014
  • In this paper, we investigated the use of seasonal autoregressive integrated moving average (SARIMA) time series models for fault detection in semiconductor etch equipment data. The derivative dynamic time warping algorithm was employed for the synchronization of data. The models were generated using a set of data from healthy runs, and the established models were compared with the experimental runs to find the faulty runs. It has been shown that the SARIMA modeling for this data can detect faults in the etch tool data from the semiconductor industry with an accuracy of 80% and 90% using the parameter-wise error computation and the step-wise error computation, respectively. We found that SARIMA is useful to detect incipient faults in semiconductor fabrication.

A Prediction of Northeast Asian Summer Precipitation Using Teleconnection (원격상관을 이용한 북동아시아 여름철 강수량 예측)

  • Lee, Kang-Jin;Kwon, MinHo
    • Atmosphere
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    • v.25 no.1
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    • pp.179-183
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    • 2015
  • Even though state-of-the-art general circulation models is improved step by step, the seasonal predictability of the East Asian summer monsoon still remains poor. In contrast, the seasonal predictability of western North Pacific and Indian monsoon region using dynamic models is relatively high. This study builds canonical correlation analysis model for seasonal prediction using wind fields over western North Pacific and Indian Ocean from the Global Seasonal Forecasting System version 5 (GloSea5), and then assesses the predictability of so-called hybrid model. In addition, we suggest improvement method for forecast skill by introducing the lagged ensemble technique.

Adaptive Reconstruction of Harmonic Time Series Using Point-Jacobian Iteration MAP Estimation and Dynamic Compositing: Simulation Study

  • Lee, Sang-Hoon
    • Korean Journal of Remote Sensing
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    • v.24 no.1
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    • pp.79-89
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    • 2008
  • Irregular temporal sampling is a common feature of geophysical and biological time series in remote sensing. This study proposes an on-line system for reconstructing observation image series contaminated by noises resulted from mechanical problems or sensing environmental condition. There is also a high likelihood that during the data acquisition periods the target site corresponding to any given pixel may be covered by fog or cloud, thereby resulting in bad or missing observation. The surface parameters associated with the land are usually dependent on the climate, and many physical processes that are displayed in the image sensed from the land then exhibit temporal variation with seasonal periodicity. A feedback system proposed in this study reconstructs a sequence of images remotely sensed from the land surface having the physical processes with seasonal periodicity. The harmonic model is used to track seasonal variation through time, and a Gibbs random field (GRF) is used to represent the spatial dependency of digital image processes. The experimental results of this simulation study show the potentiality of the proposed system to reconstruct the image series observed by imperfect sensing technology from the environment which are frequently influenced by bad weather. This study provides fundamental information on the elements of the proposed system for right usage in application.

Classification with Seasonal Variability using Harmonic Components: Application for Remotely-sensed Images of Korean Peninsula

  • Lee, Sang-Hoon;Hong, Hyun-Ki
    • Proceedings of the KSRS Conference
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    • pp.1483-1485
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    • 2003
  • Multitemporal approaches using sequential data acquired over multiple years are essential for satisfactory discrimination between many land cover classes whose signatures exhibit seasonal trends. At any particular time, the response of several classes may be indistinguishable. Using the estimates of periodogram which are obtained from sequential images, the periodicity of the process have been incorporates into multitemporal classification. The Normalized Difference Vegetation Index (NDVI) was computed for seven-day composites of the Advanced Very High Resolution Radiometer (AVHRR) imagery over the Korean peninsula for 1996 - 2000 using a dynamic technique.

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A Study on Performance of Seasonal Borehole Thermal Energy Storage System Using TRNSYS (TRNSYS를 이용한 Borehole 방식 태양열 계간축열 시스템의 성능에 관한 연구)

  • Park, Sang-Mi;Seo, Tae-Beom
    • Journal of the Korean Solar Energy Society
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    • v.38 no.5
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    • pp.37-47
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    • 2018
  • The heating performance of a solar thermal seasonal storage system applied to a glass greenhouse was analyzed numerically. For this study, the gardening 16th zucchini greenhouse of Jeollanam-do agricultural research & extension services was selected. And, the heating load of the glass greenhouse selected was 576 GJ. BTES (Borehole Thermal Energy Storage) was considered as a seasonal storage, which is relatively economical. The TRNSYS was used to predict and analyze the dynamic performance of the solar thermal system. Numerical simulation was performed by modeling the solar thermal seasonal storage system consisting of flat plate solar collector, BTES system, short-term storage tank, boiler, heat exchanger, pump, controller. As a result of the analysis, the energy of 928 GJ from the flat plate solar collector was stored into BTES system and 393 GJ of energy from BTES system was extracted during heating period, so that it was confirmed that the thermal efficiency of BTES system was 42% in 5th year. Also since the heat supplied from the auxiliary boiler was 87 GJ in 5th year, the total annual heating demand was confirmed to be mostly satisfied by the proposed system.

Learning Algorithm of Dynamic Threshold in Line Utilization based SARIMA model (SARIMA 모델을 기반으로 한 선로 이용률의 동적 임계값 학습 기법)

  • Cho, Kagn-Hong;Ahn, Seong-Jin;Chung, Jin-Wook
    • The KIPS Transactions:PartC
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    • v.9C no.6
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    • pp.841-846
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    • 2002
  • We applies a seasonal ARIMA model to the timely forecasting in a line utilization and its confidence interval on the base of the past data of the line utilization that QoS of the network is greatly influenced by. And this paper proposes the learning algorithm of dynamic threshold in line utilization using the SARIMA model. We can find the proper dynamic threshold in timely line utilization on the various network environments and provide the confidence based on probability. Also, we have evaluated the validity of the proposed model and estimated the value of a proper threshold on real network. Network manager can overcome a shortcoming of original threshold method and maximize the performance of this algorithm.