A single series representation of multiple independent ARMA processes

A Single Series Representation of MultipleIndependent ARMA Pro essesRoss

Binomial Arma Count Series from Renewal Processes

This paper describes a new method for generating stationary integervalued time series from renewal processes. We prove that if the lifetime distribution of renewal processes is nonlattice and the probability generating function is rational, then the generated time series satisfy causal and invertible ARMA type stochastic difference equations. The result provides an easy method for generating in...

Anomaly Detection in Time Series of Graphs using ARMA Processes

There are many situations in which indicators of changes or anomalies in communication networks can be helpful, e.g. in the identification of faults. A dynamic communication network is characterised as a series of graphs with vertices representing IP addresses and edges representing information exchange between these entities weighted by packets sent. Ten graph distance metrics are used to crea...

Arma Representation of Wind Field

The dynamic response analysis of structures subjected to a stochastic wind field is carried out in the time domain by a step-by-step integration approach. The loading is represented by simulated time histories of the aerodynamic force. The auto-regressive and moving average (ARMA) recursive models are utilized to simulate time series of wind loads. Depending on the system dynamic characteristic...

Discrete-valued ARMA processes

This paper presents a unified framework of stationary ARMA processes for discrete-valued time series based on Pegram’s [Pegram, G.G.S., 1980. An autoregressive model for multilag markov chains. J. Appl. Probab. 17, 350–362] mixing operator. Such a stochastic operator appears to be more flexible than the currently popular thinning operator to construct Box and Jenkins’ type stationary ARMAproces...