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- W2023229229 abstract "This paper introduces a generalized diffusion entropy analysis method to analyze long-range correlation, then applies this method to stock volatility series, traffic congestion index and daily average temperature series of Beijing. The method uses the techniques of diffusion process and Rényi entropy to focus on the scaling behaviors of regular and extreme volatility of time series. However, crossovers arising from the extrinsic periodic trend make the scaling behavior difficult to analyze. Periodic trend is found to deeply affect correlation, as series present obscure scaling behavior while exhibiting obvious scaling behavior after the trend is removed. We introduce Fourier filtering method to eliminate the trend effects and systematically investigate the multifractal long-range correlation of traffic congestion index and daily average temperature series. For daily average temperature series of Beijing, regular volatility and extreme volatility both exhibit long-range persistence. For traffic congestion index series of Beijing, regular volatility exhibits un-correlation or short correlation, while extreme volatility reveals anti-persistence." @default.
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- W2023229229 date "2014-06-01" @default.
- W2023229229 modified "2023-10-16" @default.
- W2023229229 title "Fourier Filtering for Minimizing the Periodic Trend Effects on Multifractal Diffusion Entropy Analysis" @default.
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- W2023229229 doi "https://doi.org/10.1142/s0219477514500102" @default.
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