• 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.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.325-327
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• 2003

Forest biomass is the basis of forest ecosystem. With the rapid development of remote sensing and computer technology, forest biomass estimation using remote sensing data is paid great attention and has acquired great achievements. This article focuses on discussion of methods of forest biomass estimation methods using Terra/MODIS data in Northeast China. The research include: combining the MODIS time series parameters with seasonal characteristics of forest species to identify major forest species; establishing a model to estimate forest biomass based on forest species; analyzing the effects of the existent forest biomass and increasing biomass on terrestrial carbon cycle. This research can help to make clear the mechanism of carbon cycle.

• Magazine of the Korean Society of Agricultural Engineers
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• v.31 no.4
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• pp.72-80
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• 1989

Various analyses were made to investigate the stochastic structure of the daily rainfall in Korea. Records of daily rainfall amounts from 1951 to 1984 at Chinju Metesrological Station were used for this study. Obtained results are as follows : 1. Time series of the daily rainfall at Chinju were positively, serially correlated for the lag as large as one day. 2. Rainfall events, defined as a sequence of consecutive wet days separated by one or more dry days, showed a seasonal variation in the occurrence frequency. 3. The marginal distribution of event characteristics of each month showed significant dif- ferences each other. Events occurred in summer had longer duration and higher magnitude with higher intensity than those of events occurred in winter. 4. There were significant positive correlations among four event characteristics ; dura- tion, magnitude, average intensity, and maximum intensity. 5. Correlations among the daily rainfall amounts within an event were not significant in general. 6. There were no consistant significancy in identity or difference between the distribu- tions of daily rainfall amounts for different days within events. 7. Above mentioned characteristics of daily rainfall time series must be considered in building a stochastic model of daily rainfall.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.4
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• pp.511-516
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• 1996

In this paper, weekly maximum power demand forecasting method in consideration of temperature estimation using a time series model was presented. The method removing weekly, seasonal variations on the load and irregularities variation due to unknown factor was presented. The forecasting model that represent the relations between load and temperature which get a numeral expected temperature based on the past 30 years(1961~1990) temperature was constructed. Effect of holiday was removed by using a weekday change ratio, and irregularities variation was removed by using an autoregressive model. The results of load forecasting show the ability of the method in forecasting with good accuracy without suffering from the effect of seasons and holidays. Percentage error load forecasting of all seasons except summer was obtained below 2 percentage. (author). refs., figs., tabs.

• The Korean Journal of Applied Statistics
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• v.26 no.2
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• pp.349-360
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• 2013

Forecasting the daily peak load for electricity demand is an important issue for future power plants and power management. We first introduce several time series models to predict the peak load for electricity demand and then compare the performance of models under the RMSE(root mean squared error) and MAPE(mean absolute percentage error) criteria.

• 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.

• 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.

• Journal of Astronomy and Space Sciences
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• v.30 no.4
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• pp.241-246
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• 2013

We have investigated drought periodicities occurred in Seoul to find out any indication of relationship between drought in Korea and solar activities. It is motivated, in view of solar-terrestrial connection, to search for an example of extreme weather condition controlled by solar activity. The periodicity of drought in Seoul has been re-examined using the wavelet transform technique as the consensus is not achieved yet. The reason we have chosen Seoul is because daily precipitation was recorded for longer than 200 years, which meets our requirement that analyses of drought frequency demand long-term historical data to ensure reliable estimates. We have examined three types of time series of the Effective Drought Index (EDI). We have directly analyzed EDI time series in the first place. And we have constructed and analyzed time series of histogram in which the number of days whose EDI is less than -1.5 for a given month of the year is given as a function of time, and one in which the number of occasions where EDI values of three consecutive days are all less than -1.5 is given as a function of time. All the time series data sets we analyzed are periodic. Apart from the annual cycle due to seasonal variations, periodicities shorter than the 11 year sunspot cycle, ~ 3, ~ 4, ~ 6 years, have been confirmed. Periodicities to which theses short periodicities (shorter than Hale period) may be corresponding are not yet known. Longer periodicities possibly related to Gleissberg cycles, ~ 55, ~ 120 years, can be also seen. However, periodicity comparable to the 11 year solar cycle seems absent in both EDI and the constructed data sets.

• Journal of Korean Society on Water Environment
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• v.26 no.2
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• pp.243-251
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• 2010

Evapotranspiration and rainfall-runoff are the major components of hydrological cycle and thereby the changes of them can directly affect the wetness/dryness or runoff characteristics of basins. In this study the wetness/dryness in Geum river basin are classified by dint of cumulative probability density function of monthly moisture index and the long term changes of them are analyzed based on climatic water balance concept. The drought events in Geum river basin are selected through evaluation of monthly moisture index and the various hydrological properties of them are investigated in detail. Also the trends of time-series of climatic water balance components are examined by Seasonal Kendall test and the variability of hydrological cycle in Geum river basin during the recent decade is inquired. It is judged that the results of this study can be contributed to establishment of the counter plan against the future drought events as the fundamental information.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1640-1644
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• 2010

In this study the wetness/dryness in Geum river basin are classified by dint of cumulative probability density function of monthly moisture index and the long term changes of them are analyzed based on climatic water balance concept. The drought events in Geum river basin are selected through evaluation of monthly moisture index and the various hydrological properties of them are investigated in detail. Also the trends of time-series of climatic water balance components are examined by Seasonal Kendall test and the variability of hydrological cycle in Geum river basin during the recent decade is inquired. It is judged that the results of this study can be contributed to establishment of the counter plan against the future drought events as the fundamental information.