The evaluation of infectious and noninfectious disease management can be done through the use of a time series analysis. In this study, we expect to measure the results and prevent intervention effects on the disease. Clinical studies have benefited from the use of these techniques, particularly for the wide applicability of the ARIMA model. This study briefly presents the process of using the ...

We address the problem of estimating changes in fractional integrated ARMA (FARIMA) processes. These changes may be in the Long Range Dependence (LRD) parameter or the ARMA parameters. The signal is divided into “elementary” segments: the objective is then to estimate the segments in which the changes occur. This estimation is achieved by minimizing a penalized least-squares criterion based on ...

Forecasting is of prime importance for accuracy in decision-making. For data sets containing high autocorrelations, failure to account for temporal dependence will result in poor forecasting. TES (Transform-Expand-Sample) is a class of stochastic processes to model empirical autocorrelated time series and is frequently used in Monte Carlo simulation. Its merit is to capture simultaneously both ...

Seasonal ARIMA model is a good traffic model capable of capturing the behavior of a network traffic stream. In this paper, we give a general expression of seasonal ARIMA models with two periodicities and provide procedures to model and to predict traffic using seasonal ARIMA models. The experiments conducted in our feasibility study showed that seasonal ARIMA models can be used to model and pre...

BACKGROUND Cases of hemorrhagic fever with renal syndrome (HFRS) are widely distributed in eastern Asia, especially in China, Russia, and Korea. It is proved to be a difficult task to eliminate HFRS completely because of the diverse animal reservoirs and effects of global warming. Reliable forecasting is useful for the prevention and control of HFRS. METHODS Two hybrid models, one composed of...

BACKGROUND Accurate forecasting of hospital outpatient visits is beneficial for the reasonable planning and allocation of healthcare resource to meet the medical demands. In terms of the multiple attributes of daily outpatient visits, such as randomness, cyclicity and trend, time series methods, ARIMA, can be a good choice for outpatient visits forecasting. On the other hand, the hospital outpa...

Many environmental and socioeconomic time–series data can be adequately modeled using Auto-Regressive Integrated Moving Average (ARIMA) models. We call such time–series ARIMA time–series. We consider the problem of clustering ARIMA time–series. We propose the use of the Linear Predictive Coding (LPC) cepstrum of time–series for clustering ARIMA time–series, by using the Euclidean distance betwe...

4. Course Outline: (i) Review of Linear ARMA/ARIMA Time Series Models and their Properties. (ii) An Introduction to Spectral Analysis of Time Series. (iii) Fractional Differencing and Long Memory Time Series Modelling. (iv) Generalized Fractional Processes. Gegenbaur Processes. (v) Topics from Financial Time Series/Econometrics: ARCH and GARCH Models. (vi ) Time Series Modelling of Durations: A...