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Artykuł dostępny w postaci pełnego tekstu - kliknij by otworzyć plik
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Autoregressive error-processes, cubic splines and tridiagonal matrices

100%
Drygas H.
Discussiones Mathematicae Probability and Statistics
|
2003
|
tom 23
|
nr 2
147-165
EN
In the paper formulate for the inversion of some tridiagonal matrices are given. The results can be applied to the autoregressive processes.
2
Content available remote

Forecasting time series with multivariate copulas

100%
Simard C., Rémillard B.
Dependence Modeling
|
2015
|
tom 3
|
nr 1
EN
In this paper we present a forecasting method for time series using copula-based models for multivariate time series. We study how the performance of the predictions evolves when changing the strength of the different possible dependencies, as well as the structure of the dependence. We also look at the impact of the marginal distributions. The impact of estimation errors on the performance of the predictions is also considered. In all the experiments, we compare predictions from our multivariate method with predictions from the univariate version which has been introduced in the literature recently. To simplify implementation, a test of independence between univariate Markovian time series is proposed. Finally, we illustrate the methodology by a practical implementation with financial data.
3
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Exponential smoothing and resampling techniques in time series prediction

88%
Neves M., Cordeiro C.
Discussiones Mathematicae Probability and Statistics
|
2010
|
tom 30
|
nr 1
87-101
EN
Time series analysis deals with records that are collected over time. The objectives of time series analysis depend on the applications, but one of the main goals is to predict future values of the series. These values depend, usually in a stochastic manner, on the observations available at present. Such dependence has to be considered when predicting the future from its past, taking into account trend, seasonality and other features of the data. Some of the most successful forecasting methods are based on the concept of exponential smoothing. There are a variety of methods that fall into the exponential smoothing family, each having the property that forecasts are weighted combinations of past observations. But time series analysis needs proper statistical modeling. The model that better describes the behavior of the series in study can be crucial in obtaining 'good' forecasts. Departures from the true underlying distribution can adversely affect those forecasts. Resampling techniques have been considered in many situations to overcome that difficulty. For time series, several authors have proposed bootstrap methodologies. Here we will present an automatic procedure built in R language that first selects the best exponential smoothing model (among a set of possibilities) for fitting the data, followed by a bootstrap approach for obtaining forecasts. A real data set has been used to illustrate the performance of the proposed procedure.
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