Recent analyses of serial correlations in cognitive tasks have provided preliminary evidence
of the presence of a particular form of long-range serial dependence known as 1/f noise. It …

Modelling structural breaks, long memory and stock market volatility: an overview - ScienceDirect
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Long-memory, or more generally fractal, processes are known to play an important role in
many scientific disciplines and applied fields such as physics, geophysics, hydrology …

With the integration of wind energy into electricity grids, it is becoming increasingly important
to obtain accurate wind speed/power forecasts. Accurate wind speed forecasts are …

This is the second edition of Climate Time Series Analysis which was first published in 2010.
In this digital age, a second edition means not that the first edition of a book has been sold …

The increasing availability of financial market data at intraday frequencies has not only led to
the development of improved volatility measurements but has also inspired research into …

A self-contained, contemporary treatment of the analysis of long-range dependent data Long-
Memory Time Series: Theory and Methods provides an overview of the theory and methods …

In recent years, with the availability of high-frequency financial market data modeling
realized volatility has become a new and innovative research direction. The construction of …

Gender differences in vote choice, opinion, and party identification have become a common
feature of the American political landscape. We examine the nature and causes of gender …

Novel periodic extensions of dynamic long-memory regression models with autoregressive
conditional heteroscedastic errors are considered for the analysis of daily electricity spot …