With the increasing competition in the telecommunications industry, the operators try their best to increase telecom income via various measures, one of which is to set an amount of income as a goal to make the encouragement. Since accurate forecast of income plays an important role in income target setting, this paper builds a time series Autoregressive Integrated Moving Average Model (ARIMA) ...

For example, it has long been recognized that single exponential forecasting (SES) is equivalent to an ARIMA(0,1,1) model (e.g., Harvey, 1989) The additional flexibility of ARIMA models may be thought to lead to more accurate empirical forecasts. However, Table 13 of Makridakis and Hibon shows that there is virtually no improvement in forecasting accuracy using ARIMA models (labeled B-J automat...

Analyses from some of the highway agencies show that up to 50% permanent traffic counts (PTCs) have missing values. It will be difficult to eliminate such a significant portion of data from traffic analysis. Literature review indicates that the limited research uses factor or autoregressive integrated moving average (ARIMA) models for predicting missing values. Factor-based models tend to be le...

The predictability of network traffic is a significant interest in many domains such as congestion control, admission control, and network management. An accurate traffic prediction model should have the ability to capture prominent traffic characteristics, such as long-range dependence (LRD) and self-similarity in the large time scale, multifractal in small time scale. In this paper we propose...

This report surveys time series methods that have been used and can be applied in predicting end-to-end delay of the Internet. ARIMA scheme and state-space approach are discussed and compared. Although state-space approach has the advantages in structure and computation, ARIMA modeling is still useful in identifying systems due to the complexity and uncertainty of the Internet. A practical exam...

The well-known Box-Jenkins’ Autoregressive Integrated Moving Average (ARIMA) methodology for fitting time-series data has some major limitations. To this end, Exponential Autoregressive (EXPAR) family of models may be employed. An important characteristic feature of EXPAR is that it is capable of modelling those data sets that depict cyclical variations. Further, it can also be used when data s...

Abstract Evapotranspiration (ET) is an important process in the hydrological cycle and needs to be accurately quantified for proper irrigation scheduling and optimal water resources systems operation. The time variant characteristics of ET necessitate the need for forecasting ET. In this paper, two techniques, namely a seasonal ARIMA model and Winter's exponential smoothing model, have been inv...

AIM To study the application of artificial neural network (ANN) in forecasting the incidence of hepatitis A, which had an autoregression phenomenon. METHODS The data of the incidence of hepatitis A in Liaoning Province from 1981 to 2001 were obtained from Liaoning Disease Control and Prevention Center. We used the autoregressive integrated moving average (ARIMA) model of time series analysis ...

Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mini...

Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to in...