• Title/Summary/Keyword: Seasonal Time Series

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Forecasting daily peak load by time series model with temperature and special days effect (기온과 특수일 효과를 고려하여 시계열 모형을 활용한 일별 최대 전력 수요 예측 연구)

  • Lee, Jin Young;Kim, Sahm
    • The Korean Journal of Applied Statistics
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    • v.32 no.1
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    • pp.161-171
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    • 2019
  • Varied methods have been researched continuously because the past as the daily maximum electricity demand expectation has been a crucial task in the nation's electrical supply and demand. Forecasting the daily peak electricity demand accurately can prepare the daily operating program about the generating unit, and contribute the reduction of the consumption of the unnecessary energy source through efficient operating facilities. This method also has the advantage that can prepare anticipatively in the reserve margin reduced problem due to the power consumption superabundant by heating and air conditioning that can estimate the daily peak load. This paper researched a model that can forecast the next day's daily peak load when considering the influence of temperature and weekday, weekend, and holidays in the Seasonal ARIMA, TBATS, Seasonal Reg-ARIMA, and NNETAR model. The results of the forecasting performance test on the model of this paper for a Seasonal Reg-ARIMA model and NNETAR model that can consider the day of the week, and temperature showed better forecasting performance than a model that cannot consider these factors. The forecasting performance of the NNETAR model that utilized the artificial neural network was most outstanding.

Seasonal Variation of Planktonic Foraminifera Assemblage in response to Seasonal Shift of Inter-Tropical Convergence Zone in the Northeastern Equatorial Pacific (적도수렴대의 위치변화에 따른 북동태평양 적도해역의 부유성 유공충 군집의 계절변동)

  • Lee, Yuri;Asahi, Hirofumi;Woo, Han Jun;Kim, Hyung Jeek;Lee, Seong-Joo;Khim, Boo-Keun
    • Ocean and Polar Research
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    • v.36 no.4
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    • pp.437-445
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    • 2014
  • A time-series sediment trap was operated at a water depth of 4950 m from July 2003 to May 2004 at KOMO station ($10^{\circ}30^{\prime}N$, $131^{\circ}20^{\prime}W$) in the northeastern equatorial Pacific, with the aim of understanding the temporal variation of planktonic foraminifera assemblages in response to the seasonal shift of Inter-Tropical Convergence Zone (ITCZ). A total of 22130 planktonic foraminifera specimens belonging to 30 species and 11 genera were identified, which shows a distinct seasonal variation with high values (125~288 specimens $m^{-2}day^{-1}$) in the winter to spring (December-May) and low values (16~23 specimens $m^{-2}day^{-1}$) in the fall (September-November). In addition, seasonal ecological differences of foraminifera assemblages are distinctly recognizable: omnivorous foraminifera occurred predominantly during the summer season, whereas herbivorous ones were dominant during the winter season. Such seasonal variations correspond to the seasonal shift of the ITCZ. Enhanced occurrence of herbivorous species during the winter-spring season seems a result of surface water mixing generated by the southward shift of the ITCZ. The increase in omnivorous species during the summer season may be due to the northward movement of the ITCZ caused by weakened wind speed, resulting in the intensification of water column stratification and nutrient-poor environment. A significant reduction of planktonic foraminifera specimens during the fall is attributed to heavy precipitation and reduction in light intensity.

Regression models based on cumulative data for forecasting of new product (신제품 수요예측을 위하여 누적자료를 활용한 회귀모형에 관한 연구)

  • Park, Sang-Gue;Oh, Jung-Hyun
    • Journal of the Korean Data and Information Science Society
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    • v.20 no.1
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    • pp.117-124
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    • 2009
  • If time series data with seasonal effect exist, various statistical models like winters for successful forecasts could be used. But if the data are not enough to estimate seasonal effect, not much methods are available. This paper proposes the statistical forecasting method based on cumulative data when the data are not enough to estimate seasonal effect. We apply this method to real cosmetic sales data and show its better performance over moving average method.

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Seasonal Variations of Physical Conditions and Currents in the Sea Near Gadeok-Sudo (가덕수도 근해에서 물리적 현상과 해류의 계절 변동)

  • Jang, Sung-Tae;Jeon, Dong-Chull;Shin, Chang-Woong
    • Ocean and Polar Research
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    • v.30 no.1
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    • pp.33-46
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    • 2008
  • In order to investigate seasonal variations of the physical environments in the region of Jinhae Bay-Nakdongpo, we carried out hydrographic surveys from November 2000 to November 2001. Horizontal and vertical distribution of salinity and temperature shows large seasonal variations. Water column is well mixed in winter and stratified in summer. Low-salinity water is distributed in the form of patches because of the drainage control at the Nakdong River. Seasonal variations in the sea near Gadeok-Sudo are affected by topography, river discharge and tidal current. Currents have been measured using a bottom mounted ADCP and DCM12 between November 2000 and August 2001 in the Gadeok-Sudo. The current in the Gadeok-Sudo shows a distinct two-layer structure with reversed current. Low-pass filtered time series of wind, sea elevation and current are coherent for the period of 1-2 days and are attributed to Ekman-like dynamics. Spatial and temporal circulation pattern shows a slight different. The subtidal current in Jinhae Bay goes northward, however is reversed in the Gadeok-Sudo mouth.

A Study on the Demand Forecasting and Efficient Operation of Jeju National Airport using seasonal ARIMA model (계절 ARIMA 모형을 이용한 제주공항 여객 수요예측 및 효율적 운영에 관한 연구)

  • Kim, Kyung-Bum;Hwang, Kyung-Soo
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.13 no.8
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    • pp.3381-3388
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    • 2012
  • This research is to find out the method appropriate for the forecasting of passennger demand using seasonal ARIMA model and efficient operation in Jeju National Airport. Time series monthly data for the investigation were collected ranging from January 2003 to December 2011. A total of 108 observations were used for data analysis. Research findings showed that the multiplicative seasonal ARIMA(0.1.2)(0.1.1)12 model is appropriate model. The number of passengers in Jeju National Airport will continue to rise, it was expected to surpass 20 million people.

The AADT estimation through time series analysis using irregular factor decomposition method (불규칙변동 분해 시계열분석 기법을 사용한 AADT 추정)

  • 이승재;백남철;권희정;최대순;도명식
    • Journal of Korean Society of Transportation
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    • v.19 no.6
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    • pp.65-73
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    • 2001
  • Until recently, we use only weekly and monthly adjustment factors in order to estimate the AADT. By the way. we can suppose that the traffic is time series data related to flow of time. So we tried to analyse traffic patterns using time series analysis and apply them to estimate the AADT. We could divide traffic patterns into trend, cyclic variation, seasonal variation and irregular variation like as time series data. Also, in order to reduce random error components, we have looked for the weather conditions as an influential factor. There are many weather conditions such as rainfalls, but, temperatures, and sunshine hours among others but we selected rainfalls and lowest temperatures. And then, we have estimated the AADT using time series factors. To compare the results of, we have applied both irregular variation joined to weather factors and that not joined to. RMSE and U-test were opted at methods to appreciate results of AADT estimation.

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Estimating Heterogeneous Customer Arrivals to a Large Retail store : A Bayesian Poisson model perspective (대형할인매점의 요일별 고객 방문 수 분석 및 예측 : 베이지언 포아송 모델 응용을 중심으로)

  • Kim, Bumsoo;Lee, Joonkyum
    • Korean Management Science Review
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    • v.32 no.2
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    • pp.69-78
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    • 2015
  • This paper considers a Bayesian Poisson model for multivariate count data using multiplicative rates. More specifically we compose the parameter for overall arrival rates by the product of two parameters, a common effect and an individual effect. The common effect is composed of autoregressive evolution of the parameter, which allows for analysis on seasonal effects on all multivariate time series. In addition, analysis on individual effects allows the researcher to differentiate the time series by whatevercharacterization of their choice. This type of model allows the researcher to specifically analyze two different forms of effects separately and produce a more robust result. We illustrate a simple MCMC generation combined with a Gibbs sampler step in estimating the posterior joint distribution of all parameters in the model. On the whole, the model presented in this study is an intuitive model which may handle complicated problems, and we highlight the properties and possible applications of the model with an example, analyzing real time series data involving customer arrivals to a large retail store.

Time series analysis for incidence of scarlet fever in children in Jeju Province, Korea, 2002~2016 (2002~2016년도 제주도 소아의 성홍열 발생의 시계열분석)

  • Shin, In-Hye;Bae, Jong-Myon
    • Journal of Medicine and Life Science
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    • v.16 no.3
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    • pp.90-95
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    • 2019
  • The Korea Centers for Diseases Control and Prevention interpreted that recent outbreaks of scarlet fever in Korea since 2011 was resulted from the expansion of scarlet fever notification criteria. To suggest a relevant hypothesis regarding this emerging outbreak, a time series analysis(TSA) of scarlet fever incidence between 2002 and 2016 was conducted. The raw data was the nationwide insurance claims database administered by the Korean National Health Insurance Service. The inclusion criteria were children aged ≤14 years residing in Jeju Province, Korea who received any form of healthcare for scarlet fever from 2002 to 2016. The season was defined as winter (December, January, February; Q1), spring (March, April, May; Q2), summer (June, July, August; Q3), and autumn (September, October, November; Q4). There were seasonal variations with showing peak season on Q1 and Q3. And three phases as 2002 Q2~2005 Q2, 2005 Q2~2009 Q4, and 2010 Q1~2016 Q4 were found between 2002 and 2016. The results from TSA suggested that the recent outbreak of scarlet fever among children in Jeju Province might be a phenomenon from 'unknown birth-related environmental factors' changed after 2010.

A Least Square Fit Analysis on the Earth's Polar Motion Time Series: Implication against Smylie's Conjecture (지구의 극운동에 대한 최소제곱법 분석: 스마일리의 추측에 상반됨)

  • Chung, Tae-Woong;Na, Sung-Ho
    • Geophysics and Geophysical Exploration
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    • v.19 no.2
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    • pp.91-96
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    • 2016
  • From the Earth's polar motion time series (IERS 08 C04, since 1981), after removal of seasonal variation by band-pass filtering, we acquired Earth's free Eulerian motion (Chandler wobble) time series. By successive least square error fittings on it, we analyzed amplitude and phase variation of Chandler wobble. We attempted to identify any precursory behavior of the pole before large earthquakes but only to fail. Unlike Smylie's conjecture there was no appreciable motion of the Earth's pole detected at around the each times of recent six largest earthquakes of magnitude over 8.5.

Time-series Analysis of Geodetic Reference Frame Aligned to International Terrestrial Reference Frame

  • Bae, Tae-Suk;Hong, Chang-Ki;Lee, Jisun;Altamimi, Zuheir;Sillard, Patrick;Boucher, Claude
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.39 no.5
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    • pp.313-319
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    • 2021
  • The national geodetic reference frame of Korea was adopted in 2003, which is referenced to ITRF (International Terrestrial Reference Frame) 2000 at the epoch of January 1, 2002. For precise positioning based on the satellites, it should be thoroughly maintained to the newest global reference frame. Other than plate tectonic motion, there are significant events or changes such as earthquakes, antenna replacement, PSD (Post-Seismic Deformation), seasonal variation etc. We processed three years of GNSS (Global Navigation Satellite System) data(60 NGII CORS stations, 51 IGS core stations) to produce daily solutions minimally constrained to ITRF. From the time series of daily solutions, the sites with unexpected discontinuity were identified to set up an event(mostly antenna replacement). The combined solution with minimum constraints was estimated along with the velocity, the offsets, and the periodic signals. The residuals show that the surrounding environment also affects the time series to a certain degree, thus it should be improved eventually. The transformation parameters to ITRF2014 were calculated with stability and consistency, which means the national geodetic reference frame is properly aligned to the global reference frame.