Conditional mode regression: Application to functional time series prediction
نویسندگان
چکیده
Let us introduce n pairs of random variables (Xi, Yi)i=1,...,n that we suppose drawn from the pair (X,Y ), valued in F × IR, where F is a semi-metric space. Let d denotes the semi-metric. Assume that there exists a regular version of the conditional probability of Y given X , which is absolutely continuous with respect to Lebesgue measure on IR and has bounded density. Assume that for a corresponding author
منابع مشابه
Functional-Coefficient Autoregressive Model and its Application for Prediction of the Iranian Heavy Crude Oil Price
Time series and their methods of analysis are important subjects in statistics. Most of time series have a linear behavior and can be modelled by linear ARIMA models. However, some of realized time series have a nonlinear behavior and for modelling them one needs nonlinear models. For this, many good parametric nonlinear models such as bilinear model, exponential autoregressive model, threshold...
متن کاملFunctional Methods for Time Series Prediction: A Nonparametric Approach
The problem of prediction in time series using nonparametric functional techniques is considered. An extension of the local linear method to regression with functional explanatory variable is proposed. This forecasting method is compared with the functional Nadaraya–Watson method and with fi nitedimensional nonparametric predictors for several real-time series. Prediction intervals based on the...
متن کاملFast Nonparametric Conditional Density Estimation
Conditional density estimation. The idea of conditional density estimation is to construct a density estimate f̂(y|x) for a dependent variable y, conditional on a vector of variables x. This can be seen as a generalization of regression, where instead of estimating the expected value E(y|x) alone, we instead model the full density. This is especially important for multi-modal densities, where th...
متن کاملEnsemble Kernel Learning Model for Prediction of Time Series Based on the Support Vector Regression and Meta Heuristic Search
In this paper, a method for predicting time series is presented. Time series prediction is a process which predicted future system values based on information obtained from past and present data points. Time series prediction models are widely used in various fields of engineering, economics, etc. The main purpose of using different models for time series prediction is to make the forecast with...
متن کاملSome New Methods for Prediction of Time Series by Wavelets
Extended Abstract. Forecasting is one of the most important purposes of time series analysis. For many years, classical methods were used for this aim. But these methods do not give good performance results for real time series due to non-linearity and non-stationarity of these data sets. On one hand, most of real world time series data display a time-varying second order structure. On th...
متن کامل