scholarly journals Topological data analysis of financial time series: Landscapes of crashes

2018 ◽  
Vol 491 ◽  
pp. 820-834 ◽  
Author(s):  
Marian Gidea ◽  
Yuri Katz
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Financial Time Series ◽  
Topological Data Analysis ◽  
Financial Time ◽  
Topological Data

scholarly journals Topological Data Analysis Approaches to Uncovering the Timing of Ring Structure Onset in Filamentous Networks

2021 ◽  
Vol 83 (3) ◽  
Author(s):  
Maria-Veronica Ciocanel ◽  
Riley Juenemann ◽  
Adriana T. Dawes ◽  
Scott A. McKinley
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Actin Filaments ◽  
Time Series Data ◽  
Polymer Networks ◽  
Topological Data Analysis ◽  
Series Data ◽  
Topological Features ◽  
Topological Data

AbstractIn developmental biology as well as in other biological systems, emerging structure and organization can be captured using time-series data of protein locations. In analyzing this time-dependent data, it is a common challenge not only to determine whether topological features emerge, but also to identify the timing of their formation. For instance, in most cells, actin filaments interact with myosin motor proteins and organize into polymer networks and higher-order structures. Ring channels are examples of such structures that maintain constant diameters over time and play key roles in processes such as cell division, development, and wound healing. Given the limitations in studying interactions of actin with myosin in vivo, we generate time-series data of protein polymer interactions in cells using complex agent-based models. Since the data has a filamentous structure, we propose sampling along the actin filaments and analyzing the topological structure of the resulting point cloud at each time. Building on existing tools from persistent homology, we develop a topological data analysis (TDA) method that assesses effective ring generation in this dynamic data. This method connects topological features through time in a path that corresponds to emergence of organization in the data. In this work, we also propose methods for assessing whether the topological features of interest are significant and thus whether they contribute to the formation of an emerging hole (ring channel) in the simulated protein interactions. In particular, we use the MEDYAN simulation platform to show that this technique can distinguish between the actin cytoskeleton organization resulting from distinct motor protein binding parameters.

Inferring Temporal Phenotypes with Topological Data Analysis and Pseudo Time-Series

2019 ◽  
pp. 399-409 ◽  
Author(s):  
Arianna Dagliati ◽  
Nophar Geifman ◽  
Niels Peek ◽  
John H. Holmes ◽  
Lucia Sacchi ◽  
...  
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Topological Data Analysis ◽  
Topological Data

scholarly journals Forecasting the volatility of Financial Time Series by Tree Ensembles

2019 ◽  
Vol 12 (3) ◽  
pp. 82-89
Author(s):  
O. S. Vidmant
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Capital Investment ◽  
Time Series Data ◽  
Financial Time Series ◽  
Series Data ◽  
Time Interval ◽  
Short Term ◽  
Computing Power ◽  
Financial Time

The use of new tools for economic data analysis in the last decade has led to significant improvements in forecasting. This is due to the relevance of the question, and the development of technologies that allow implementation of more complex models without resorting to the use of significant computing power. The constant volatility of the world indices forces all financial market players to improve risk management models and, at the same time, to revise the policy of capital investment. More stringent liquidity and transparency standards in relation to the financial sector also encourage participants to experiment with protective mechanisms and to create predictive algorithms that can not only reduce the losses from the volatility of financial instruments but also benefit from short-term investment manipulations. The article discusses the possibility of improving the efficiency of calculations in predicting the volatility by the models of tree ensembles using various methods of data analysis. As the key points of efficiency growth, the author studied the possibility of aggregation of financial time series data using several methods of calculation and prediction of variance: Standard, EWMA, ARCH, GARCH, and also analyzed the possibility of simplifying the calculations while reducing the correlation between the series. The author demonstrated the application of calculation methods on the basis of an array of historical price data (Open, High, Low, Close) and volume indicators (Volumes) of futures trading on the RTS index with a five-minute time interval and an annual set of historical data. The proposed method allows to reduce the cost of computing power and time for data processing in the analysis of short-term positions in the financial markets and to identify risks with a certain level of confidence probability.

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