Understanding of the stability of deterministic and stochastic dynamical systems has evolved recently from a traditional grounding in the system’s normal modes to a more comprehensive foundation in the system’s propagator and especially in an appreciation for the role of non-normality of the dynamical operator in determining the system’s stability as revealed through the propagator. This set of...

The nature is composed of infinite process, and each process is surely deterministic (out of mention for the micro process on the level of quantum physics, which is under the uncertainty principle of Heisenberg, 1927), but affected by uncountable number of factors. What we are trying to do with modeling is to find the most dominating factors on a process and to simplify the process with an unde...

We analyzed the cross-correlation of Photovoltaic (PV) output fluctuation for the actual PV output time series data in both the Tokyo area and the whole of Japan using the principal component analysis with the random matrix theory. Based on the obtained cross-correlation coefficients, the forecast error for PV output was estimated with/without considering the cross-correlations. Then operation ...

An objective method of determining and correcting phase or position errors in numerical weather prediction is described and tested in a radar data observing system simulation experiment (OSSE) addressing the forecasting of ongoing thunderstorms. Such phase or position errors are common in numerical forecasts at grid resolutions of 1-to-20 km (meso-γ scale). It is proposed that the process of co...

The primary objective of this study is to examine the association between the quality of mandatory earnings forecast, measured by forecast accuracy and bias, and the ownership structure of Taiwanese firms, measured by the divergence between the ultimate owner’s control and the equity ownership level. The study is based on 528 forecasts issued by Taiwanese listed firms from 1999 to 2001 which we...

This paper puts forward a prediction model based on membrane computing optimization algorithm for chaos time series; the model optimizes simultaneously the parameters of phase space reconstruction (τ, m) and least squares support vector machine (LS-SVM) (γ, σ) by using membrane computing optimization algorithm. It is an important basis for spectrum management to predict accurately the change tr...

Wind powerfluctuations at the turbine and farm scales are generally not expected to be correlated over large distances.When power fromdistributed farms feeds the electrical grid,fluctuations from various farms are expected to smooth out. Using data from the Irish grid as a representative example, we analyze wind power fluctuations entering an electrical grid.We find that not only are grid-scale...

When a forecast ft,h of a variable Yt+h is made at time t for h periods ahead, the loss (or cost) will arise if a forecast turns to be different from the actual value. The loss function of the forecast error et+h = Yt+h − ft,h, is denoted as c(Yt+h, ft,h). The loss function can depend on the time of prediction and so it can be ct+h(Yt+h, ft,h). If the loss function is not changing with time and...

Damped trend exponential smoothing models have gained importance in empirical studies due to their remarkable forecasting performance. This paper derives their theoretical forecast error variance, based on the implied ARIMA model, as algebraic function of the structural parameters. As a consequence, the minimum mean squared error (MMSE) forecasts as well as the h-step ahead theoretical forecast...