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    Application of wavelet decomposition in time-series forecasting
    (Elsevier Science Sa, 2017) Zhang, Keyi; Gencay, Ramazan; Yazgan, M. Ege
    Observed time series data can exhibit different components, such as trends, seasonality, and jumps, which are characterized by different coefficients in their respective data generating processes. Therefore, fitting a given time series model to aggregated data can be time consuming and may lead to a loss of forecasting accuracy. In this paper, coefficients for variable components in estimations are generated based on wavelet-based multiresolution analyses. Thus, the accuracy of forecasts based on aggregate data should be improved because the constraint of equality among the model coefficients for all data components is relaxed. (C) 2017 Elsevier B.V. All rights reserved.
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    How Successful Are Wavelets in Detecting Jumps?
    (MDPI AG, 2017-12) Eroğlu, Burak Alparslan; Gencay, Ramazan; Yazgan, Ege
    We evaluate the performances of wavelet jump detection tests by using simulated high-frequency data, in which jumps and some other non-standard features are present. Wavelet-based jump detection tests have a clear advantage over the alternatives, as they are capable of stating the exact timing and number of jumps. The results indicate that, in addition to those advantages, these detection tests also preserve desirable power and size properties even in non-standard data environments, whereas their alternatives fail to sustain their desirable properties beyond standard data features.