The Application of Stochastic Bifurcation Theory to the Early Detection of Economic Bubbles

Dmitriev, A. V.; Dmitriev, Victor; Sagaydak, Oleg; Tsukanova, Olga A.

doi:10.1016/j.procs.2017.11.380

Cited by 6 publications

(7 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Economics and management sub-subjects of LIS field are also conducted research by using time series analysis. The papers have focused on telecommunication infrastructure and its relations to economic growth/activity (Cronin et al 1991;Dutta 2001), disseminating economic census data (Zeisset 1998) and early detection of an economic bubble (Dmitriev et al 2017). The last subject category, social media, can be accepted as a part of management subject.…”

Section: Time Series Analysis Studies In Lismentioning

confidence: 99%

Forecasting the future of library and information science and its sub-fields

Taşkın

2020

Scientometrics

View full text Add to dashboard Cite

Forecasting is one of the methods applied in many studies in the library and information science (LIS) field for numerous purposes, from making predictions of the next Nobel laureates to potential technological developments. This study sought to draw a picture for the future of the LIS field and its sub-fields by analysing 97 years of publication and citation patterns. The core Web of Science indexes were used as the data source, and 123,742 articles were examined in-depth for time series analysis. The social network analysis method was used for sub-field classification. The field was divided into four sub-fields: (1) librarianship and law librarianship, (2) health information in LIS, (3) scientometrics and information retrieval and (4) management and information systems. The results of the study show that the LIS sub-fields are completely different from each other in terms of their publication and citation patterns, and all the sub-fields have different dynamics. Furthermore, the number of publications, references and citations will increase significantly in the future. It is expected that more scholars will work together. The future subjects of the LIS field show astonishing diversity from fake news to predatory journals, open government, e-learning and electronic health records. However, the findings prove that publish or perish culture will shape the field. Therefore, it is important to go beyond numbers. It can only be achieved by understanding publication and citation patterns of the field and developing research policies accordingly.

show abstract

Section: Time Series Analysis Studies In Lismentioning

confidence: 99%

Forecasting the future of library and information science and its sub-fields

Taşkın

2020

Scientometrics

View full text Add to dashboard Cite

show abstract

“…This is due to it first appearing in 𝐺 (3,5) and then disappearing at 𝐺 (4,5) with corresponding persistence pair (4, 4.5). The cycle then reappears at 𝐺 (5,7) and disappears at 𝐺 (8,9) resulting in persistence pair at (6, 8.5).…”

Section: Examplementioning

confidence: 99%

“…Outside of detecting imminent events, many other important characteristics of a system are studied through the lens of dynamics. These include population models transitioning from stable values to chaotic oscillations based on environmental factors [1], economic bubbles showing dynamics with bimodal distribution bifurcations [8], or chaotic fluctuations in power-grid dynamics through period-doubling [9].…”

Section: Introductionmentioning

confidence: 99%

Temporal Network Analysis Using Zigzag Persistence

Myers¹,

Khasawneh²,

Munch³

2022

Preprint

View full text Add to dashboard Cite

This work presents a framework for studying temporal networks using zigzag persistence, a tool from the field of Topological Data Analysis (TDA). The resulting approach is general and applicable to a wide variety of time-varying graphs. For example, these graphs may correspond to a system modeled as a network with edges whose weights are functions of time, or they may represent a time series of a complex dynamical system. We use simplicial complexes to represent snapshots of the temporal networks that can then be analyzed using zigzag persistence. We show two applications of our method to dynamic networks: an analysis of commuting trends on multiple temporal scales, e.g., daily and weekly, in the Great Britain transportation network, and the detection of periodic/chaotic transitions due to intermittency in dynamical systems represented by temporal ordinal partition networks. Our findings show that the resulting zero-and one-dimensional zigzag persistence diagrams can detect changes in the networks' shapes that are missed by traditional connectivity and centrality graph statistics.

show abstract

“… 1 , 2 Various examples have been observed in diverse research fields and include collapses of ecosystems, 3 , 4 sudden climate shifts 5 , 6 or financial crashes. 7 , 8 Abrupt regime shifts have particularly been theorized and observed in systems biology and medicine, 9 , 10 , 11 at the onset of certain disease states like atrial fibrillation 12 or epileptic seizures, 13 as well as in biological processes like regulation of gene networks 14 , 15 and cell fate decisions, 16 , 17 including epithelial-mesenchymal transitions. 18 Correctly detecting and alerting for these critical changes allows us to better understand complex developments and to anticipate dangerous outcomes.…”

Section: Introductionmentioning

confidence: 99%