Gaussian ARMA processes with continuous time parameter, otherwise known as stationary continuous-time Gaussian processes with rational spectral density , have been of interest for many years. In the last twenty years there has been a resurgence of interest in continuous-time processes, partly as a result of the very successful application of stochastic diierential equation models to problems in...

Let Xt be an /-dimensional ARMA (p, q) process. Let g: U l -> W be a measurable function. Define a process Zt by Zt = g(Xt). Generally, Z.is not an ARMA process. However, we are interested in such functions g, for which Zt is also an AR process. It is important to know the orders of the process Zt. In the most cases we find only some bounds for them. From the practical point of view, our consid...

The Normalized Prediction Error, or NPE, can be used for the evaluation of the fit of AR models, which are estimated from signals that are generated by an AR process. The NPE does not only provide a measure of the time domain fit, but also of the frequency domain fit of an estimated model. Therefore, it is a measure that can very well be used for comparison of different estimates. In this paper...

In this paper, the design of dynamic neural networks with Kalman filter is proposed and applied to identify nonlinear dynamic systems. The optimal parameters of a dynamic neural network, which contains several autoregressive moving average (ARMA) sub models, weighs and biases, are obtained using the well known delta rule. Using the obtained parameters of the ARMA sub models, a new dynamic netwo...

We introduce the new time series analysis features of scikits.statsmodels. This includes descriptive statistics, statistical tests and several linear model classes, autoregressive, AR, autoregressive moving-average, ARMA, and vector autoregressive models VAR.

In this paper, we investigate whether incorporating common factors of CPI sub-aggregates into forecasting models increases the accuracy of forecasts of inflation. We extract factors by both static and dynamic factor models and then embed them in ARMA and VAR models. Using quarterly data of Iran’s CPI and its sub-aggregates, the models are estimated over 1990:2 to 2008:2 and out of sample ...