Long-Range Dependence and Self-Similarity

Discover the intricate world of time series analysis with Long-Range Dependence and Self-Similarity by Vladas Pipiras. Published by Cambridge University Press in 2017, this comprehensive hardback edition spans an impressive 688 pages. This essential resource delves into the complexities of real-world time series, which often defy simple assumptions and display long-range dependence. Ignoring these characteristics can lead to significant inaccuracies in trend detection and understanding critical behaviors. Designed for graduate students and researchers in statistics and probability, this book also serves as a valuable reference for specialists across various fields, including economics, finance, and hydrology. Enhance your analytical skills and deepen your understanding of Gaussian processes and stochastic processes with this authoritative text.