• Title/Summary/Keyword: ARIMA Seasonal Model

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A study on demand forecasting for Jeju-bound tourists by travel purpose using seasonal ARIMA-Intervention model (계절형 ARIMA-Intervention 모형을 이용한 여행목적 별 제주 관광객 수 예측에 관한 연구)

  • Song, Junmo
    • Journal of the Korean Data and Information Science Society
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    • v.27 no.3
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    • pp.725-732
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    • 2016
  • This study analyzes the number of Jeju-bound tourists according to travellers' purposes. We classify the travellers' purposes into three categories: "Rest and Sightseeing", "Leisure and Sport", and "Conference and Business". To see an impact of MERS outbreak occurred in May 2015 on the number of tourists, we fit seasonal ARIMA-Intervention model to the monthly arrivals data from January 2005 to March 2016. The estimation results show that the number of tourists for "Leisure and Sport" and "Conference and Business" were significantly affected by MERS outbreak whereas arrivals for "Rest and Sightseeing" were little influenced. Using the fitted models, we predict the number of Jeju-bound tourists.

Comparison of time series predictions for maximum electric power demand (최대 전력수요 예측을 위한 시계열모형 비교)

  • Kwon, Sukhui;Kim, Jaehoon;Sohn, SeokMan;Lee, SungDuck
    • The Korean Journal of Applied Statistics
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    • v.34 no.4
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    • pp.623-632
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    • 2021
  • Through this study, we studied how to consider environment variables (such as temperatures, weekend, holiday) closely related to electricity demand, and how to consider the characteristics of Korea electricity demand. In order to conduct this study, Smoothing method, Seasonal ARIMA model and regression model with AR-GARCH errors are compared with mean absolute error criteria. The performance comparison results of the model showed that the predictive method using AR-GARCH error regression model with environment variables had the best predictive power.

Solar Power Generation Forecast Model Using Seasonal ARIMA (SARIMA 모형을 이용한 태양광 발전량 예보 모형 구축)

  • Lee, Dong-Hyun;Jung, Ahyun;Kim, Jin-Young;Kim, Chang Ki;Kim, Hyun-Goo;Lee, Yung-Seop
    • Journal of the Korean Solar Energy Society
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    • v.39 no.3
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    • pp.59-66
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    • 2019
  • New and renewable energy forecasts are key technology to reduce the annual operating cost of new and renewable facilities, and accuracy of forecasts is paramount. In this study, we intend to build a model for the prediction of short-term solar power generation for 1 hour to 3 hours. To this end, this study applied two time series technique, ARIMA model without considering seasonality and SARIMA model with considering seasonality, comparing which technique has better predictive accuracy. Comparing predicted errors by MAE measures of solar power generation for 1 hour to 3 hours at four locations, the solar power forecast model using ARIMA was better in terms of predictive accuracy than the solar power forecast model using SARIMA. On the other hand, a comparison of predicted error by RMSE measures resulted in a solar power forecast model using SARIMA being better in terms of predictive accuracy than a solar power forecast model using ARIMA.

Deep Learning Based Prediction Method of Long-term Photovoltaic Power Generation Using Meteorological and Seasonal Information (기후 및 계절정보를 이용한 딥러닝 기반의 장기간 태양광 발전량 예측 기법)

  • Lee, Donghun;Kim, Kwanho
    • The Journal of Society for e-Business Studies
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    • v.24 no.1
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    • pp.1-16
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    • 2019
  • Recently, since responding to meteorological changes depending on increasing greenhouse gas and electricity demand, the importance prediction of photovoltaic power (PV) is rapidly increasing. In particular, the prediction of PV power generation may help to determine a reasonable price of electricity, and solve the problem addressed such as a system stability and electricity production balance. However, since the dynamic changes of meteorological values such as solar radiation, cloudiness, and temperature, and seasonal changes, the accurate long-term PV power prediction is significantly challenging. Therefore, in this paper, we propose PV power prediction model based on deep learning that can be improved the PV power prediction performance by learning to use meteorological and seasonal information. We evaluate the performances using the proposed model compared to seasonal ARIMA (S-ARIMA) model, which is one of the typical time series methods, and ANN model, which is one hidden layer. As the experiment results using real-world dataset, the proposed model shows the best performance. It means that the proposed model shows positive impact on improving the PV power forecast performance.

KTX passenger demand forecast with multiple intervention seasonal ARIMA models (다중개입 계절형 ARIMA 모형을 이용한 KTX 수송수요 예측)

  • Cha, Hyoyoung;Oh, Yoonsik;Song, Jiwoo;Lee, Taewook
    • The Korean Journal of Applied Statistics
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    • v.32 no.1
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    • pp.139-148
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    • 2019
  • This study proposed a multiple intervention time series model to predict KTX passenger demand. In order to revise the research of Kim and Kim (Korean Society for Railway, 14, 470-476, 2011) considering only the intervention of the second phase of Gyeong-bu before November of 2011, we adopted multiple intervention seasonal ARIMA models to model the time series data with additional interventions which occurred after November of 2011. Through the data analysis, it was confirmed that the effects of various interventions such as Gyeong-bu and Ho-nam 2 phase, outbreak of MERS and national holidays, which affected the KTX transportation demand, are successfully explained and the prediction accuracy could be quite improved significantly.

Stochastic Characteristics of Water Quality Variation of the Chungju Lake (충주호 수질변동의 추계학적 특성)

  • 정효준;황대호;백도현;이홍근
    • Journal of Environmental Health Sciences
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    • v.27 no.3
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    • pp.35-42
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    • 2001
  • The characteristics of water quality variation were predicted by stochastic model in Chungju dam, north Chungcheong province of south Korea, Monthly time series data of water quality from 1989 to 2001;temperature, BOD, COD and SS, were obtained from environmental yearbook and internet homepage of ministry of environment. Development of model was carried out with Box-Jenkins method, which includes model identification, estimation and diagnostic checking. ACF and PACF were used to model identification. AIC and BIC were used to model estimation. Seosonal multiplicative ARIMA(1, 0, 1)(1, 1, 0)$_{12}$ model was appropriate to explain stochastic characteristics of temperature. BOD model was ARMa(2, 2, 1), COD was seasonal multiplicative ARIMA(2. 0. 1)(1. 0, 1)$_{12}$, and SS was ARIMA(1, 0, 2) respectively. The simulated water quality data showed a good fitness to the observed data, as a result of model verification.ion.

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Forecasting with a combined model of ETS and ARIMA

  • Jiu Oh;Byeongchan Seong
    • Communications for Statistical Applications and Methods
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    • v.31 no.1
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    • pp.143-154
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    • 2024
  • This paper considers a combined model of exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) models that are commonly used to forecast time series data. The combined model is constructed through an innovational state space model based on the level variable instead of the differenced variable, and the identifiability of the model is investigated. We consider the maximum likelihood estimation for the model parameters and suggest the model selection steps. The forecasting performance of the model is evaluated by two real time series data. We consider the three competing models; ETS, ARIMA and the trigonometric Box-Cox autoregressive and moving average trend seasonal (TBATS) models, and compare and evaluate their root mean squared errors and mean absolute percentage errors for accuracy. The results show that the combined model outperforms the competing models.

A study on Estimation of NO2 concentration by Statistical model (통계모형을 이용한 NO2 농도 예측에 관한 연구)

  • Jang Nan-Sim
    • Journal of Environmental Science International
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    • v.14 no.11
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    • pp.1049-1056
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    • 2005
  • [ $NO_2$ ] concentration characteristics of Busan metropolitan city was analysed by statistical method using hourly $NO_2$ concentration data$(1998\~2000)$ collected from air quality monitoring sites of the metropolitan city. 4 representative regions were selected among air quality monitoring sites of Ministry of environment. Concentration data of $NO_2$, 5 air pollutants, and data collected at AWS was used. Both Stepwise Multiple Regression model and ARIMA model for prediction of $NO_2$ concentrations were adopted, and then their results were compared with observed concentration. While ARIMA model was useful for the prediction of daily variation of the concentration, it was not satisfactory for the prediction of both rapid variation and seasonal variation of the concentration. Multiple Regression model was better estimated than ARIMA model for prediction of $NO_2$ concentration.

Application to Evaluation of Hydrologic Time Series Forecasting for Long-Term Runoff Simulation (장기유출모의를 위한 수문시계열 예측모형의 적용성 평가)

  • Yoon, Sun-Kwon;Ahn, Jae-Hyun;Kim, Jong-Suk;Moon, Young-Il
    • Journal of Korea Water Resources Association
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    • v.42 no.10
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    • pp.809-824
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    • 2009
  • Hydrological system forecasting, which is the short term runoff historical data during the limited period in dam site, is a conditional precedent of hydrological persistence by stochastic analysis. We have forecasted the monthly hydrological system from Andong dam basin data that is the rainfall, evaporation, and runoff, using the seasonal ARIMA (autoregressive integrated moving average) model. Also we have conducted long term runoff simulations through the forecasted results of TANK model and ARIMA+TANK model. The results of analysis have been concurred to the observation data, and it has been considered for application to possibility on the stochastic model for dam inflow forecasting. Thus, the method presented in this study suggests a help to water resource mid- and long-term strategy establishment to application for runoff simulations through the forecasting variables of hydrological time series on the relatively short holding runoff data in an object basins.

A Machine Learning Univariate Time series Model for Forecasting COVID-19 Confirmed Cases: A Pilot Study in Botswana

  • Mphale, Ofaletse;Okike, Ezekiel U;Rafifing, Neo
    • International Journal of Computer Science & Network Security
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    • v.22 no.1
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    • pp.225-233
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    • 2022
  • The recent outbreak of corona virus (COVID-19) infectious disease had made its forecasting critical cornerstones in most scientific studies. This study adopts a machine learning based time series model - Auto Regressive Integrated Moving Average (ARIMA) model to forecast COVID-19 confirmed cases in Botswana over 60 days period. Findings of the study show that COVID-19 confirmed cases in Botswana are steadily rising in a steep upward trend with random fluctuations. This trend can also be described effectively using an additive model when scrutinized in Seasonal Trend Decomposition method by Loess. In selecting the best fit ARIMA model, a Grid Search Algorithm was developed with python language and was used to optimize an Akaike Information Criterion (AIC) metric. The best fit ARIMA model was determined at ARIMA (5, 1, 1), which depicted the least AIC score of 3885.091. Results of the study proved that ARIMA model can be useful in generating reliable and volatile forecasts that can used to guide on understanding of the future spread of infectious diseases or pandemics. Most significantly, findings of the study are expected to raise social awareness to disease monitoring institutions and government regulatory bodies where it can be used to support strategic health decisions and initiate policy improvement for better management of the COVID-19 pandemic.