• Title/Summary/Keyword: seasonal ARIMA(SARIMA)

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Comparison Studies of Hybrid and Non-hybrid Forecasting Models for Seasonal and Trend Time Series Data (트렌드와 계절성을 가진 시계열에 대한 순수 모형과 하이브리드 모형의 비교 연구)

  • Jeong, Chulwoo;Kim, Myung Suk
    • Journal of Intelligence and Information Systems
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    • v.19 no.1
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    • pp.1-17
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    • 2013
  • In this article, several types of hybrid forecasting models are suggested. In particular, hybrid models using the generalized additive model (GAM) are newly suggested as an alternative to those using neural networks (NN). The prediction performances of various hybrid and non-hybrid models are evaluated using simulated time series data. Five different types of seasonal time series data related to an additive or multiplicative trend are generated over different levels of noise, and applied to the forecasting evaluation. For the simulated data with only seasonality, the autoregressive (AR) model and the hybrid AR-AR model performed equivalently very well. On the other hand, if the time series data employed a trend, the SARIMA model and some hybrid SARIMA models equivalently outperformed the others. In the comparison of GAMs and NNs, regarding the seasonal additive trend data, the SARIMA-GAM evenly performed well across the full range of noise variation, whereas the SARIMA-NN showed good performance only when the noise level was trivial.

Prediction of Covid-19 confirmed number of cases using SARIMA model (SARIMA모형을 이용한 코로나19 확진자수 예측)

  • Kim, Jae-Ho;Kim, Jang-Young
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.26 no.1
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    • pp.58-63
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    • 2022
  • The daily number of confirmed cases of Coronavirus disease 2019(COVID-19) ranges between 1,000 and 2,000. Despite higher vaccination rates, the number of confirmed cases continues to increase. The Mu variant of COVID-19 reported in some countries by WHO has been identified in Korea. In this study, we predicted the number of confirmed COVID-19 cases in Korea using the SARIMA for the Covid-19 prevention strategy. Trends and seasonality were observed in the data, and the ADF Test and KPSS Test was used accordingly. Order determination of the SARIMA(p,d,q)(P, D, Q, S) model helped in extracting the values of p, d, q, P, D, and Q parameters. After deducing the p and q parameters using ACF and PACF, the data were transformed and schematized into stationary forms through difference, log transformation, and seasonality removal. If seasonality appears, first determine S, then SARIMA P, D, Q, and finally determine ARIMA p, d, q using ACF and PACF for the order excluding seasonality.

Prediction Algorithm of Threshold Violation in Line Utilization using ARIMA model (ARIMA 모델을 이용한 설로 이용률의 임계값 위반 예측 기법)

  • 조강흥;조강홍;안성진;안성진;정진욱
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.25 no.8A
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    • pp.1153-1159
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    • 2000
  • This paper 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 lido utilization that QoS of the network is greatly influenced by and proposes the prediction algorithm of threshold violation in line utilization using the seasonal ARIMA model. We can predict the time of threshold violation in line utilization 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 and a detection probability, it thus appears that we have maximized the performance of this algorithm.

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Time Series Analysis and Forecasting of Electrical Conductivity in Coastal Aquifers (연안암반대수층의 해수침투경향성 파악을 위한 전기전도도 시계열 분석과 예측)

  • Ju, Jeong-Woung;Yeo, In Wook
    • Economic and Environmental Geology
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    • v.50 no.4
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    • pp.267-276
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    • 2017
  • Seawater intrusion into coastal fractured rock aquifer, resulting in groundwater contamination, is of serious concern in coastal areas of Jeolla Namdo, Korea, which heavily depends on groundwater resources. Time series analysis and forecasting were carried out to analyze and predict EC which is a major indicator of seawater intrusion. Two time series models of autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) were tested for suggesting appropriate time series model. Time series data of EC measured over one year showed a increasing trend with short periodic fluctuations, due to tidal effect and pumping, which indicated that EC time series data tended to be non-stationary. SARIMA model was found better fitted to observed EC than any other time series model. Time series analysis and modeling was found to be a useful tool to analyze EC at coastal fractured rock aquifer subject to seawater intrusion.

Study on Forecasting Hotel Banquet Revenue by Utilizing ARIMA Model (ARIMA 모형을 이용한 호텔 연회의 매출액 예측에 관한 연구)

  • Cho, Sung-Ho;Chang, Se-Jun
    • Culinary science and hospitality research
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    • v.15 no.2
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    • pp.231-242
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    • 2009
  • One of the most crucial information at the hotel banquet is revenue data. Revenue forecast enables cost reduction, increases staffing efficiency, and provides information that helps maximizing competitive advantages in unforeseen environment. This research forecasts the hotel banquet revenue by utilizing ARIMA Model which was assessed as the appropriate forecast model for international researches. The data used for this research was based on the monthly banquet revenue data of G hotel at Seoul. The analysis results showed that SARIMA(2, 1, 3)(0, 1, 1) was finally presumed. This research implied that the ARIMA model, which was assessed as the appropriate forecast model, was applied for analyzing the monthly hotel banquet revenue data. Additionally, the research provides beneficial information with which hotel banquet professionals can utilize as a reference.

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Estimation of Layered Periodic Autoregressive Moving Average Models (계층형 주기적 자기회귀 이동평균 모형의 추정)

  • Lee, Sung-Duck;Kim, Jung-Gun;Kim, Sun-Woo
    • Communications for Statistical Applications and Methods
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    • v.19 no.3
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    • pp.507-516
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    • 2012
  • We study time series models for seasonal time series data with a covariance structure that depends on time and the periodic autocorrelation at various lags $k$. In this paper, we introduce an ARMA model with periodically varying coefficients(PARMA) and analyze Arosa ozone data with a periodic correlation in the practical case study. Finally, we use a PARMA model and a seasonal ARIMA model for data analysis and show the performance of a PARMA model with a comparison to the SARIMA model.

Forecasting Foreign Visitors using SARIMAX Models with the Exogenous Variable of Demand Decrease (수요감소 요인 외생변수를 갖는 SARIMAX 모형을 이용한 관광수요 예측)

  • Lee, Geun-Cheol;Choi, Seong-Hoon
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.43 no.4
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    • pp.59-66
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    • 2020
  • In this study, we consider the problem of forecasting the number of inbound foreigners visiting Korea. Forecasting tourism demand is an essential decision to plan related facilities and staffs, thus many studies have been carried out, mainly focusing on the number of inbound or outbound tourists. In order to forecast tourism demand, we use a seasonal ARIMA (SARIMA) model, as well as a SARIMAX model which additionally comprises an exogenous variable affecting the dependent variable, i.e., tourism demand. For constructing the forecasting model, we use a search procedure that can be used to determine the values of the orders of the SARIMA and SARIMAX. For the exogenous variable, we introduce factors that could cause the tourism demand reduction, such as the 9/11 attack, the SARS and MERS epidemic, and the deployment of THAAD. In this study, we propose a procedure, called Measuring Impact on Demand (MID), where the impact of each factor on tourism demand is measured and the value of the exogenous variable corresponding to the factor is determined based on the measurement. To show the performance of the proposed forecasting method, an empirical analysis was conducted where the monthly number of foreign visitors in 2019 were forecasted. It was shown that the proposed method can find more accurate forecasts than other benchmarks in terms of the mean absolute percentage error (MAPE).

Monthly rainfall forecast of Bangladesh using autoregressive integrated moving average method

  • Mahmud, Ishtiak;Bari, Sheikh Hefzul;Rahman, M. Tauhid Ur
    • Environmental Engineering Research
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    • v.22 no.2
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    • pp.162-168
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    • 2017
  • Rainfall is one of the most important phenomena of the natural system. In Bangladesh, agriculture largely depends on the intensity and variability of rainfall. Therefore, an early indication of possible rainfall can help to solve several problems related to agriculture, climate change and natural hazards like flood and drought. Rainfall forecasting could play a significant role in the planning and management of water resource systems also. In this study, univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) model was used to forecast monthly rainfall for twelve months lead-time for thirty rainfall stations of Bangladesh. The best SARIMA model was chosen based on the RMSE and normalized BIC criteria. A validation check for each station was performed on residual series. Residuals were found white noise at almost all stations. Besides, lack of fit test and normalized BIC confirms all the models were fitted satisfactorily. The predicted results from the selected models were compared with the observed data to determine prediction precision. We found that selected models predicted monthly rainfall with a reasonable accuracy. Therefore, year-long rainfall can be forecasted using these models.

Outliers and Level Shift Detection of the Mean-sea Level, Extreme Highest and Lowest Tide Level Data (평균 해수면 및 최극조위 자료의 이상자료 및 기준고도 변화(Level Shift) 진단)

  • Lee, Gi-Seop;Cho, Hong-Yeon
    • Journal of Korean Society of Coastal and Ocean Engineers
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    • v.32 no.5
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    • pp.322-330
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    • 2020
  • Modeling for outliers in time series was carried out using the MSL and extreme high, low tide levels (EHL, HLL) data set in the Busan and Mokpo stations. The time-series model is seasonal ARIMA model including the components of the AO (additive outliers) and LS (level shift). The optimal model was selected based on the AIC value and the model parameters were estimated using the 'tso' function (in 'tsoutliers' package of R). The main results by the model application, i.e.. outliers and level shift detections, are as follows. (1) The two AO are detected in the Busan monthly EHL data and the AO magnitudes were estimated to 65.5 cm (by typhoon MAEMI) and 29.5 cm (by typhoon SANBA), respectively. (2) The one level shift in 1983 is detected in Mokpo monthly MSL data, and the LS magnitude was estimated to 21.2 cm by the Youngsan River tidal estuary barrier construction. On the other hand, the RMS errors are computed about 1.95 cm (MSL), 5.11 cm (EHL), and 6.50 cm (ELL) in Busan station, and about 2.10 cm (MSL), 11.80 cm (EHL), and 9.14 cm (ELL) in Mokpo station, respectively.