In this article, normal inverse Gaussian (NIG) autoregressive model is introduced. The parameters of the are estimated using expectation maximization (EM) algorithm. efficacy EM algorithm shown simulated and real-world financial data. It that NIG fit very well on considered data hence could be useful in modelling various real-life time-series

Human motion prediction, which aims at predicting future human skeletons given the past ones, is a typical sequence-to-sequence problem. Therefore, extensive efforts have been devoted to exploring different RNN-based encoder-decoder architectures. However, by generating target poses conditioned on previously generated these models are prone bringing issues such as error accumulation In this pap...

We investigate the asymptotic behavior of Nadaraya-Watson (NW) estimator regression function a τ−mixing process. prove strong consistency and normality this we illustrate these two properties using simulated data.

Measurement error is omnipresent in psychological data. However, the vast majority of applications of autoregressive time series analyses in psychology do not take measurement error into account. Disregarding measurement error when it is present in the data results in a bias of the autoregressive parameters. We discuss two models that take measurement error into account: An autoregressive model...

In this paper we address the problem of enhancing speech which has been degraded by additive noise. As proposed by Ephraim et al., autoregressive hidden Markov models (AR-HMM) for the clean speech and an autoregressive Gaussian for the noise are used. The filter applied to a given frame of noisy speech is estimated using the noise model and the autoregressive Gaussian having the highest a poste...

Morehead City, North Carolina, faced a moratorium on new construction because of problems with its sewage system. We developed an autoregressive integrated moving average (ARIMA) transfer function-intervention model to help town officials explain the sources of variation in the volume of sewage-treatment-plant discharge to the state officials who had imposed the moratorium on extending the sewa...

In many intervention analysis applications, time series data may be expensive or otherwise difficult to collect. In this case the power function is helpful, because it can be used to determine the probability that a proposed intervention analysis application will detect a meaningful change. Assuming that an underlying autoregressive integrated moving average (ARIMA) or fractional ARIMA model is...

In this paper, an original Markov-switching autoregressive model is proposed to describe the space-time evolution of wind fields. At first, a non-observable process is introduced in order to model the motion of the meteorological structures. Then, conditionally to this process, the evolution of the wind fields is described by using autoregressive models whith time varying coefficients. The prop...