The modified autoregressive (mAR) index describes the clustering of shots of similar duration in a motion picture. In this paper we derive robust estimates of the mAR index for high grossing films at the US box office using a rank-based autocorrelation function resistant to the influence of outliers and compare this to estimates obtained using the classical, moment-based autocorrelation functio...

A multiple linear process with random coefficients is investigated in the paper. Conditions for existence of such process are derived and its covariance function as well as the matrix of spectral densities are calculated. The results are applied to multiple AR(1) process with random coefficients, where the matrices of coefficients can be described by a stationary process. In this case condition...

When analysing the behavior of complex networked systems, it is often the case that some components within that network are only known to the extent that they belong to one of a set of possible ”implementations” – e.g., versions of a specific protocol, class of schedulers, etc. In this report we augment the specification language considered in BUCSTR-2004-021, BUCS-TR-2005-014, BUCS-TR-2005-015...

In the study, we discussed the generalized autoregressive conditional heteroskedasticity model and enhanced it with wavelet transform to evaluate the daily returns for 1/4/2002-30/12/2011 period in Brent oil market. We proposed discrete wavelet transform generalized autoregressive conditional heteroskedasticity model to increase the forecasting performance of the generalized autoregressive cond...

We introduce the matrix exponential as a way of modelling spatially dependent data. The matrix exponential spatial specification simplifies the loglikelihood allowing a closed form solution to the problem of maximum likelihood estimation, and greatly simplifies Bayesian estimation of the model. The matrix exponential spatial specification can produce estimates and inferences similar to those fr...

A nonlinear autoregressive model, the process feedback nonlinear autoregressive (PFNAR) model, in which the autoregressive coe0cients are a function of the combination of past data, is proposed. The autoregressive coe0cients of the PFNAR model consist of sequential autoregressive parts, and a data process feedback part that feeds back the in2uence from previous data points with “signi4cant dela...

The article examines the monthly dynamics of exports, imports and balance trade between Ukraine Romania in period from 2005 to 2021. Time series 2015 2021 were used for modelling forecasting (since date European Union–Ukraine Association Agreement took effect). Adequate models Box-Jenkins methodology built: additive with seasonal component ARIMA (Autoregressive Integrated Moving Average) ARIMAS...

The application of neural networks to non-linear dynamic system identification tasks has a long history, which consists mostly autoregressive approaches. Autoregression, the usage model outputs previous time steps, is method transferring state between not necessary for modeling systems with modern network structures, such as gated recurrent units (GRUs) and Temporal Convolutional Networks (TCNs...