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...

This study aims to integrate a broad spectrum of ocean-atmospheric variables predict sea level variation along West Peninsular Malaysia coastline using machine learning and deep techniques. 4 scenarios different combinations such as surface temperature, salinity, density, atmospheric pressure, wind speed, total cloud cover, precipitation data were used train ARIMA, SVR LSTM neural network model...

A fractional normal inverse Gaussian (FNIG) process is a fractional Brownian motion subordinated to an inverse Gaussian process. This paper shows how the FNIG process emerges naturally as the limit of a random walk with correlated jumps separated by i.i.d. waiting times. Similarly, we show that the NIG process, a Brownian motion subordinated to an inverse Gaussian process, is the limit of a ran...

In this paper, we propose a computationally effective approach to detect multiple structural breaks in the mean occurring at unknown dates. We present a non-parametric approach that exploits, in the framework of least squares regression trees, the contiguity property of data generating processes in time series data. The proposed approach is applied first to simulated data and then to the Quarte...

This paper presents a synthetic traffic modeling approach to generate bursty, long-range dependent (LRD) traffic flows for ATM network simulations. The approach uses Fractional-ARIMA processes and a set of mathematical transformations to generate traffic streams with a wide range of user-specified traffic characteristics. With this modeling approach, the user can control the short-range and lon...

enables to reconstruct the whole sequence X1, ...,Xn (rather than the sample up to a permutation) makes of Pn a very flexible tool to represent complex and highly non-symmetric statistics. One can, for instance think of two-sample sequential rank statistics, V -statistics or fractional ARIMA processes as treated in Barbe and Broniatowski [1997] , [1998a] and [1998b]. In these references, sequen...

Abstract: We present a method for optimizing adaptation laws that are generalizations of the LMS algorithm. Timevarying parameters of linear regression models are estimated in situations where the regressors are stationary or have slowly time-varying properties. The parameter variations are modeled as ARIMA-processes and the aim is to use such prior information to provide high performance filte...

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...