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1
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.
2
Content available remote

Adaptive prediction of stock exchange indices by state space wavelet networks

88%
Brdyś M., Borowa A., Idźkowiak P., Brdyś M.
International Journal of Applied Mathematics and Computer Science
|
2009
|
tom 19
|
nr 2
337-348
EN
The paper considers the forecasting of the Warsaw Stock Exchange price index WIG20 by applying a state space wavelet network model of the index price. The approach can be applied to the development of tools for predicting changes of other economic indicators, especially stock exchange indices. The paper presents a general state space wavelet network model and the underlying principles. The model is applied to produce one session ahead and five sessions ahead adaptive predictors of the WIG20 index prices. The predictors are validated based on real data records to produce promising results. The state space wavelet network model may also be used as a forecasting tool for a wide range of economic and non-economic indicators, such as goods and row materials prices, electricity/fuel consumption or currency exchange rates.
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.
4
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Review of the book ,,The analysis and forecasting of time series. Practical introduction on the basis of the R environment" by: A. Zagdanski and A. Suchwałko

75%
Burnecki K.
Mathematica Applicanda
|
2016
|
tom 44
|
nr 2
PL
Niniejsza książka stanowi praktyczne wprowadzenie do modelowania w środowisku R różnorodnych danych zbieranych w regularnych odstępach czasu. Książka adresowana jest do wszystkich zainteresowanych modelami szeregów czasowych a szczególnie do studentów i absolwentów kierunków ścisłych, ekonomicznych oraz technicznych.
EN
This book provides a practical introduction to the R environment variety of modeling data collected at regular intervals. The book is addressed to anyone interested in time series models, and mainly to students and graduates of scientific, economic and technical faculties. 
5
Content available remote

Decomposition of the symptom observation matrix and grey forecasting in vibration condition monitoring of machines

63%
Cempel C.
International Journal of Applied Mathematics and Computer Science
|
2008
|
tom 18
|
nr 4
569-579
EN
With the tools of modern metrology we can measure almost all variables in the phenomenon field of a working machine, and many of the measured quantities can be symptoms of machine conditions. On this basis, we can form a symptom observation matrix (SOM) intended for condition monitoring and wear trend (fault) identification. On the other hand, we know that contemporary complex machines may have many modes of failure, called faults. The paper presents a method of the extraction of the information about faults from the symptom observation matrix by means of singular value decomposition (SVD), in the form of generalized fault symptoms. As the readings of the symptoms can be unstable, the moving average of the SOM is applied with success. An attempt to assess the diagnostic contribution of a primary symptom is made, and also an approach to assess the symptom limit value and to connect the SVD methodology with neural nets is considered. Finally, a condition forecasting problem is discussed and an application of grey system theory (GST) to symptom prognosis is presented. These possibilities are illustrated by processing data taken directly from the machine vibration condition monitoring area.
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