With service types and requirements of broadband satellite internet continuously increasing, improving QoS (Quality of Service) of satellite internet has attracted extensive attention. To reduce the impact of selfsimilarity caused by various of service traffic sources converging on satellite communication system, this paper establishes a novel model from the perspective of self-similar traffic ...

In this paper the water quality forecasting at the Nanjinguan water quality monitoring station of Yangtze River, China, is presented. The time series data used are weekly water quality data obtained directly from Nanjinguan station measurements over the course of five years. In order to forecast water quality, hybrid models consisting of Autoregressive Integrated Moving Average (ARIMA) models a...

A multiple linear regression and ARIMA hybrid model is proposed for new bug prediction depending upon resolved bugs and other available parameters of the open source software bug report. Analysis of last five year bug report data of a open source software “worldcontrol” is done to identify the trends followed by various parameters. Bug report data has been categorized on monthly basis and forec...

This paper modeled the monthly exchange rate between the Ghana Cedi and the US Dollar and forecast future rates using time series analysis. ARIMA model was developed using Box and Jenkins method of Time Series Analysis on the monthly data collected from January, 1994 to December 2010 and validated. The result showed that the predicted rates were consistent with the depreciating trend of the obs...

Background Accurate prediction of traffic flow is an integral component in most of the Intelligent Transportation Systems (ITS) applications. The data driven approach using Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) models reported in most studies demands sound database for model building. Hence, the applicability of these models remains a question in places where the data ava...

This paper discusses the prediction of inflation rate in Indonesia. The data used this research is assumed to have both linear and non-linear components. ARIMA model selected accommodate component, while ANFIS method accounts for component data. Thus, known as hybrid ARIMA-ANFIS model. clustering performed using Fuzzy C-Mean (FMS) with a Gaussian membership function. Consider 2 6 clusters. opti...

The purpose of this work is discovering regularities in financial time series using Inductive Logic Programming (ILP) and related "Discovery" software system [Vityaev et al., 1992,1993] in data mining. Discovered regularities were used for forecasting the target variable, representing the relative difference in percent between today's closing price and the price five days ahead. We describe the...

This article attempts to present a basic method of time series analysis, modelling and forecasting performance of ARIMA, GARCH (1,1) and mixed ARIMA GARCH (1,1) models using historical daily close price downloaded through the yahoo finance website from the NASDAQ stock exchange for GE company (USA) during the period of 2001 to 2014. This paper also presents a brief analysis technique introducti...

To scientifically predict the future energy demand of Shandong province, this study chose the past energy demand of Shandong province during 1995–2015 as the research object. Based on building model data sequences, the GM-ARIMA model, the GM (1,1) model, and the ARIMA model were used to predict the energy demand of Shandong province for the 2005–2015 data, the results of which were then compare...

Summary The Rubin Causal Model (RCM) is a framework that allows to define the causal effect of an intervention as contrast potential outcomes. In recent years, several methods have been developed under RCM estimate effects in time series settings. None these makes use autoregressive integrated moving average (ARIMA) models, which are instead very common econometrics literature. this paper, we p...