2017/10/13 by Uri Hasson, Jacopo Iacovacci, Hasson, Uri +11
Biochemistry, Genetics and Molecular Biology · Computer Science · Economics, Econometrics and Finance · Physics and Astronomy · #Complex Network Analysis Techniques #Complex Systems and Time Series Analysis #Data Analysis #FOS: Biological sciences #FOS: Physical sciences #Neurons and Cognition (q-bio.NC) #Quantitative Methods (q-bio.QM) #Statistics and Probability (physics.data-an) #Time Series Analysis and Forecasting #physics.data-an #q-bio.NC #q-bio.QM
paper · pdf · doi:10.48550/arxiv.1710.04947
arxiv created 2017/10/13 · openalex publication_date 2017/10/13 · arxiv updated 2017/10/16 · openalex created_date 2022/10/05 · openalex updated_date 2026/07/28
We explore a combinatorial framework which efficiently quantifies the asymmetries between minima and maxima in local fluctuations of time series. We firstly showcase its performance by applying it to a battery of synthetic cases. We find rigorous results on some canonical dynamical models (stochastic processes with and without correlations, chaotic processes) complemented by extensive numerical simulations for a range of processes which indicate that the methodology correctly distinguishes different complex dynamics and outperforms state of the art metrics in several cases. Subsequently, we apply this methodology to real-world problems emerging across several disciplines including cases in neurobiology, finance and climate science. We conclude that differences between the statistics of local maxima and local minima in time series are highly informative of the complex underlying dynamics and a graph-theoretic extraction procedure allows to use these features for statistical learning purposes.