Wavelets as Basis Functions in the Adaptation’s Methods: Author’s Model for Forecasting Short-Term

Hadaś-Dyduch, Monika

doi:10.17265/1537-1506/2016.01.002

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…Innovative algorithms will be extended by different algorithms, including those presented in (Biernacki 2007(Biernacki , 2009). Other analyses of wavelet analysis are presented, among others, in (Hadaś-Dyduch 2015, 2016b, 2016c, which also presents comparative analyses and interesting conclusions.…”

Section: Resultsmentioning

confidence: 98%

“…Wavelets are basic functions for the wavelet transform (see Hadaś-Dyduch 2015, 2016a, 2016b, Hadaś-Dyduch, Balcerzak & Pietrzak 2016. As previously mentioned, the wavelet transform is a transformation similar to Fourier transform, thus "(...) wavelet coefficients can be calculated in the same way as Fourier coefficients, by using basis functions in an inner product calculation.…”

Section: Wavelet Transformmentioning

confidence: 99%

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Approximating Financial Time Series with Wavelets

Hadaś-Dyduch

2017

AOC

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Financial time series show many characteristic properties including the phenomenon of clustering of variance, fat-tail distribution, and negative correlation between the rates of return and the volatility of their variance. These facts often render standard methods of parameter estimation and forecasting ineffective. An important feature of financial time series is that they can be characterized by long samples. This causes the models used for their estimation to potentially be more extensive.The aim of the article is to use wavelets to approximate and predict a series. The article describes the author's model for financial time forecasting and provides basic information about wavelets necessary for proper understanding of the proposed wavelet algorithm. The algorithm uses a Daubechies wavelet.

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Section: Resultsmentioning

confidence: 98%

Section: Wavelet Transformmentioning

confidence: 99%

Approximating Financial Time Series with Wavelets

Hadaś-Dyduch

2017

AOC

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“…2). Daubechies, 1992;Hadaś-Dyduch 2014a, 2015a, 2015b, 2015c, 2016aHadaś-Dyduch & Hadaś, 2017)) from:…”

Section: Empirical Researchmentioning

confidence: 99%

Nonlinear Alleviation of Edge Effects in the Context of Minimizing Prediction Errors

Hadaś-Dyduch¹

2018

IJEF

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The aim of this article is to describe so-called "edge effects" in the context of wavelet analysis. The problem of "edge effects" is displayed in cases where the filter length is greater than 2. This is due to the fact that the calculation of the wavelet coefficients for the development of the last signal of finite elements, the filter -should theoretically move beyond the signal. The article describes different ways to solve this problem using an authored approach. One of the ways presented in the article is an innovative approach in terms of "edge effects". The author's proposal is based on the nonlinear function of the trend after the division of the series into smaller units. The results obtained show that in comparison with other methods, the author's method reduces errors. In this article, the Daubechies wavelet was used for the study. The Daubechies wavelets are a family of orthogonal wavelets, characterized by a maximum number of vanishing moments for a given support.

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“…Among models based on mathematical formulas expressed explicitly, we can identify, among others, nonparametric estimators and neural predictors (see: Biernacki, 2009). Other interesting models are those based on neural networks and wavelet analysis (see: Daubechies 1989, Hadaś-Dyduch, 2014, 2015a, 2015b, 2015c, 2016a, 2016b, 2016cMeyer, Sellan & Taqqu, 1999, Vidakovic, Mueller, 1994. Also interesting are algorithms that use ARIMA, ARMA.…”

Section: Introductionmentioning

confidence: 99%

Efficiency of Authored Mixed Prediction Model with Application to the Labor Market

Hadaś-Dyduch¹

2018

EMR

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The aim of this article is to evaluate the prediction of time series using a model containing wavelets. The research hypothesis is: “Models that take into account wavelets are an effective tool for predicting employment”. To verify the hypothesis, an original model was devised. The model is based on wavelet analysis with Daubechies wavelets and an exponential alignment model. The exponential alignment model been appropriately modified by the introduction of wavelet functions. The results obtained show that a model that partially includes wavelets is an effective tool in the prediction and analysis of employment

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Wavelets as Basis Functions in the Adaptation’s Methods: Author’s Model for Forecasting Short-Term

Cited by 3 publications

References 5 publications

Approximating Financial Time Series with Wavelets

Approximating Financial Time Series with Wavelets

Nonlinear Alleviation of Edge Effects in the Context of Minimizing Prediction Errors

Efficiency of Authored Mixed Prediction Model with Application to the Labor Market

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