Incomplete time series prediction using max-margin classification of data with absent features.
Inference Robustness of ARIMA Models under Non-Normality - Special Applications to Stock Price Data.
Intelligent financial time series forecasting: A complex neuro-fuzzy approach with multi-swarm intelligence
Financial investors often face an urgent need to predict the future. Accurate forecasting may allow investors to be aware of changes in financial markets in the future, so that they can reduce the risk of investment. In this paper, we present an intelligent computing paradigm, called the Complex Neuro-Fuzzy System (CNFS), applied to the problem of financial time series forecasting. The CNFS is an adaptive system, which is designed using Complex Fuzzy Sets (CFSs) whose membership functions are complex-valued...
Interpolation and forecasting in Poisson's processes.
Invariant measures of the pair: state, approximate filtering process
Kalman filter with a non-linear non-Gaussian observation relation.
The dynamic linear model with a non-linear non-Gaussian observation relation is considered in this paper. Masreliez's theorem (see Masreliez's (1975)) of approximate non-Gaussian filtering with linear state and observation relations is extended to the case of a non-linear observation relation that can be approximated by a second-order Taylor expansion.
Kalman filter with variance components
Limit theorems for Banach-valued autoregressive processes. Applications to real continuous time processes.
Linear filtering of systems with memory and application to finance.
Linear prediction of long-range dependent time series
We present two approaches for linear prediction of long-memory time series. The first approach consists in truncating the Wiener-Kolmogorov predictor by restricting the observations to the last k terms, which are the only available data in practice. We derive the asymptotic behaviour of the mean-squared error as k tends to +∞. The second predictor is the finite linear least-squares predictor i.e. the projection of the forecast value on the last k observations. It is shown that these two predictors...
L'inverse d'une matrice d'intervalles
Mean squared errors of prediction by kriging in linear models with errors.
Minimax prediction of the sum of processes
Mixturas de distribuciones normales con aplicaciones a problemas estadísticos complejos.
Modelling stock returns with AR-GARCH processes.
Financial returns are often modelled as autoregressive time series with random disturbances having conditional heteroscedastic variances, especially with GARCH type processes. GARCH processes have been intensely studied in financial and econometric literature as risk models of many financial time series. Analyzing two data sets of stock prices we try to fit AR(1) processes with GARCH or EGARCH errors to the log returns. Moreover, hyperbolic or generalized error distributions occur to be good models...
MSE of the best linear predictor in nonorthogonal models
Multisensor estimation: New distributed algorithms.
Nonlinear filtering for LIDAR signal processing.
Nonlinear filtering for observations on a random vector field along a random path. Application to atmospheric turbulent velocities
To filter perturbed local measurements on a random medium, a dynamic model jointly with an observation transfer equation are needed. Some media given by PDE could have a local probabilistic representation by a Lagrangian stochastic process with mean-field interactions. In this case, we define the acquisition process of locally homogeneous medium along a random path by a Lagrangian Markov process conditioned to be in a domain following the path and conditioned to the observations. The nonlinear...
Nonlinear filtering for reflecting diffusions in random environments via nonparametric estimation.