Chaotic Dynamics Analysis Based on Financial Time Series

Gu, Zheng Di; Xu, Yun

doi:10.1155/2021/2373423

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…There are a number of diversified approaches to managing different processes over time, for example, optimization, formalization of operator sequences, etc. [33][34][35][36][37][38].…”

Section: Analysis Of Time Series Outputsmentioning

confidence: 99%

“…The prerequisite is the incorporation of the functions of the economics of energy intensity of the implemented processes into the calculation TAB Project . Phase portraits comparison may show hidden economic relations (cointegration, causality) in the project structure [37]. Graphical interpretation is used in other methods as a close returns test, which predicates the existence of an unstable situation [33].…”

Section: Phase Portrait Speed and Accelerationmentioning

confidence: 99%

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Time–Cost Schedules and Project–Threats Indication

et al. 2022

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One of the most common disciplines in a business or economic project is timing and resource review. Despite the frequency of use, the level of sophistication is not high enough to maintain its level of importance. Exceeding deadlines and non-compliance with contractual costs is more than common. Moreover, there are projects where uncertainties are a naturally accompanying phenomenon. Research projects, implementation of solutions in a time-limited situation, or in an environment of limited knowledge creates risk. Any project proposal faces future realization risks when its planning management does not know with certainty where the current risks and uncertainties may come from. Decision-making, risk management dynamics, and simulations have developed in recent decades into an erudite and useful discipline. The aim is to indicate how much of the time–cost schedule proposal is stable, controllable, and economically feasible. The approach is based on the idea that modern resource scheduling requires nonlinear dynamic calculating models and simulations. The methodology presented is based on the dynamics of underlying physical and economic processes that form a spatial pattern of a time series. The article’s objective is devoted to the early indication of a dynamic project schedule’s instability and predisposition to bifurcation and chaos. In other words, the aim is to show not only what will happen but how diverse and damaging the project may become in the future.

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“…There are a number of diversified approaches to managing different processes over time, for example, optimization, formalization of operator sequences, etc. [33][34][35][36][37][38].…”

Section: Analysis Of Time Series Outputsmentioning

confidence: 99%

Section: Phase Portrait Speed and Accelerationmentioning

confidence: 99%

Time–Cost Schedules and Project–Threats Indication

et al. 2022

View full text Add to dashboard Cite

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Analysis of local system behavior in the foreign exchange-market using neural networks and Monte-Carlo method for predictıon and risk assessment

Aşırım

Salepçioğlu

2023

SN Appl. Sci.

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In this study, we attempt to show the reason behind the poor estimation of the future values of foreign exchange-rate (FXR) signals under difference-equation modeling, using the neural network approach for evaluating the local system coefficients. To do this, we have splitted EUR/USD and AUD/CAD signals into many small-segments and modeled each segment as the signal representation of a linear time-invariant (LTI) system using the simple linear difference equation (LDE) formulation. After a precise segmentation of each FXR signal such that an LTI system based modeling is highly accurate in each segment, it is observed that the coefficient values of the corresponding LDEs are highly volatile, which indicates that a reliable estimation would be very difficult using LDE modeling. Although the LDE coefficients are usually observed to take values within a small range over a given FXR signal segment (sample-set), frequent sudden-jumps in coefficient values do occur, which subjects/forces the dynamics of FXR signals to undertake the dynamics of these sudden-jumps rather than the dynamics of any other deterministic or stochastic process. To support this observation, the range of variation of the LDE coefficients over each segment is analyzed to quantify the volatility of the foreign-exchange market for risk assessment.

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Multi-Attn BLS: Multi-head attention mechanism with broad learning system for chaotic time series prediction

Xiong

Yang

2023

Applied Soft Computing

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Chaotic Dynamics Analysis Based on Financial Time Series

Cited by 3 publications

References 30 publications

Time–Cost Schedules and Project–Threats Indication

Time–Cost Schedules and Project–Threats Indication

Analysis of local system behavior in the foreign exchange-market using neural networks and Monte-Carlo method for predictıon and risk assessment

Multi-Attn BLS: Multi-head attention mechanism with broad learning system for chaotic time series prediction

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