Wireless traffic modeling and prediction using seasonal ARIMA models
نویسندگان
چکیده
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 predict actual wireless traffic such as GSM traffic in China. key words: traffic modeling, prediction, seasonal ARIMA models
منابع مشابه
Comparative evaluation of ARIMA and ANFIS for modeling of wireless network traffic time series
Network traffic modeling significantly affects various considerations in networking, including network resource allocation, quality of service provisioning, network traffic management, congestion control, and bandwidth efficiency. These are very important issues in network protocol design, too. In this paper, a comprehensive comparison of modeling approaches of adaptive neuro fuzzy inference sy...
متن کاملTime-Series Modeling For Forecasting Vehicular Traffic Flow in Dublin
The traffic flow at an arterial intersection in a congested urban transportation network in the city of Dublin is modelled in this paper. Three different time-series models, viz. random walk model, Holt-Winters’ exponential smoothing technique and seasonal ARIMA model are used for modeling of traffic flow in Dublin. Simulation and short-term forecasting of univariate traffic flow data are done ...
متن کاملShort-term traffic flow prediction using seasonal ARIMA model with limited input data
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...
متن کاملTraffic Modeling and Prediction using ARIMA/GARCH Model
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...
متن کاملPrediction of the Type and Amount of Surface Water Pollutants using Time Series Models (ARIMA) and L-THIA Model (Case Study: Namrood Sub-Basin, Hablehrood Watershed)
Due to the important role of non-point source pollution in water resources management, in this study time series modeling was applied to forecast water quality parameters and L-THIA model (one type of non-point source pollution models) was applied to estimate water pollutants. The purpose of this study was to compare results of L-THIA model and ARIMA models in Namrood sub-basin located in ...
متن کامل