Joanna Bruzda's research while affiliated with Nicolaus Copernicus University and other places
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Publications (2)
In the paper we advocate the use of wavelet analysis in classifying assets and sectors as defensive (less dependent on the business cycle and the state of the market) or cyclical (highly influenced by the general level of economic activity or the variability of broad market indices). We demonstrate that such tools of bivariate wavelet analysis like...
In the paper we evaluate the usability of certain wavelet based signal estimation techniques for forecasting economic time series. We concentrate on extracting stochastic signals lying in white noise with the help of wavelet denoising techniques based on the non-decimated version of the discrete wavelet transform. The methods we suggest here can be...
Citations
... The algorithm provides accurate forecasts compared to other benchmark forecasting models. Bruzda (2014) assumes that the underlying signal is stochastic, and uses scaling of wavelet coefficients for denoising. The performance of the denoising step followed by a forecasting algorithm is studied using simulated time series, namely stochastic stationary and non-stationary processes buried in additive Gaussian white noise, as well as 16 M3 competition time series. ...
