Chaos based portfolio selection: A nonlinear dynamics approach

Spelta, Alessandro; Pecora, Nicolò; Pagnottoni, Paolo

doi:10.1016/j.eswa.2021.116055

Cited by 16 publications

(4 citation statements)

References 34 publications

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“…When going to PSR, the most important thing is the computation of the Lyapunov exponent (Spelta et al. , 2022; Rosenstein et al.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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Explainable boosted combining global and local feature multivariate regression model for deformation prediction during braced deep excavations

Zhang,

Shi,

Wang

et al. 2023

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PurposeAn accurate prediction of the deformation of retaining structures is critical for ensuring the stability and safety of braced deep excavations, while the high nonlinear and complex nature of the deformation makes the prediction challenging. This paper proposes an explainable boosted combining global and local feature multivariate regression (EB-GLFMR) model with high accuracy, robustness and interpretability to predict the deformation of retaining structures during braced deep excavations.Design/methodology/approachDuring the model development, the time series of deformation data is decomposed using a locally weighted scatterplot smoothing technique into trend and residual terms. The trend terms are analyzed through multiple adaptive spline regressions. The residual terms are reconstructed in phase space to extract both global and local features, which are then fed into a gradient-boosting model for prediction.FindingsThe proposed model outperforms other established approaches in terms of accuracy and robustness, as demonstrated through analyzing two cases of braced deep excavations.Research limitations/implicationsThe model is designed for the prediction of the deformation of deep excavations with stepped, chaotic and fluctuating features. Further research needs to be conducted to expand the model applicability to other time series deformation data.Practical implicationsThe model provides an efficient, robust and transparent approach to predict deformation during braced deep excavations. It serves as an effective decision support tool for engineers to ensure the stability and safety of deep excavations.Originality/valueThe model captures the global and local features of time series deformation of retaining structures and provides explicit expressions and feature importance for deformation trends and residuals, making it an efficient and transparent approach for deformation prediction.

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“…When going to PSR, the most important thing is the computation of the Lyapunov exponent (Spelta et al. , 2022; Rosenstein et al.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…4.2.4 Residual sequence prediction of retaining structures deformation. When going to PSR, the most important thing is the computation of the Lyapunov exponent (Spelta et al, 2022;Rosenstein et al, 1993). It is a useful tool to estimate the amount of chaos in a system.…”

Section: Eb-glfmr Modelmentioning

confidence: 99%

Explainable boosted combining global and local feature multivariate regression model for deformation prediction during braced deep excavations

Zhang,

Shi,

Wang

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Chaos is a non-linear dynamical phenomenon that exists in a wide variety of natural fields [3][4][5][6], such as biology, meteorology, and economics. Interestingly, chaos is not a pure disorder but rather an ordered state that does not possess periodic changes and other notable symmetrical features.…”

Section: Introductionmentioning

confidence: 99%

An image encryption algorithm based on a 3D chaotic Hopfield neural network and random row–column permutation

et al. 2023

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This study proposes a novel color image encryption algorithm based on a 3D chaotic Hopfield neural network and random row–column permutation. First, a 3D chaotic Hopfield neural network is proposed to produce the random sequence for generating the diffusion and permutation keys. Then, the rows and columns of the original image are randomly arranged according to the permutation key in the permutation process. Three subgraphs are formed by separating the R, G, and B components of the color image in the diffusion process. Each of the three subgraphs is split along the columns to form three parts; the left and middle parts are exchanged. Three diffusion keys are used to encrypt each of the three parts. Finally, the individually encrypted subgraphs are stitched together to obtain the final encrypted image. Simulation results using MATLAB and FPGA and security analysis demonstrate that the encryption scheme has good performance.

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“…Regarding the analysis and prediction research of nonlinear time series, it has been widely applied in time series dynamics fields such as geological change prediction [9], power load forecasting [10], financial market forecasting [11], traffic flow forecasting [12], and so on. Wang et al [13], based on traffic flow time series, improved the CAO method to determine the reconstructed phase space embedding dimension value, and used a genetic algorithm to optimize the RBF neural network to predict the reconstructed time series.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Time Series Analysis and Prediction of General Aviation Accidents Based on Multi-Timescales

et al. 2023

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General aviation accidents have complex interactions and influences within them that cannot be simply explained and predicted by linear models. This study is based on chaos theory and uses general aviation accident data to conduct research on different timescales (HM-scale, ET-scale, and EF-scale). First, time series are constructed by excluding seasonal patterns from the statistics of general aviation accidents. Secondly, the chaotic properties of multi-timescale series are determined by the 0–1 test and Lyapunov exponent. Finally, by introducing the sparrow search algorithm and tent chaotic mapping, a CSSA-LSSVM prediction model is proposed. The accident data of the National Transportation Safety Board (NTSB) of the United States in the past 15 years is selected for case analysis. The results show that the phase diagram of the 0–1 test presents Brownian motion characteristics, and the maximum Lyapunov exponents of the three scales are all positive, proving the chaotic characteristics of multi-timescale series. The CSSA-LSSVM prediction model’s testing results illustrate its superiority in time series predicting, and when the timescale declines, the prediction error reduces gradually while the fitting effect strengthens and then decreases. This study uncovers the nonlinear chaotic features of general aviation accidents and demonstrates the significance of multi-timescale research in time series analysis and prediction.

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Chaos based portfolio selection: A nonlinear dynamics approach

Cited by 16 publications

References 34 publications

Explainable boosted combining global and local feature multivariate regression model for deformation prediction during braced deep excavations

Explainable boosted combining global and local feature multivariate regression model for deformation prediction during braced deep excavations

An image encryption algorithm based on a 3D chaotic Hopfield neural network and random row–column permutation

Nonlinear Time Series Analysis and Prediction of General Aviation Accidents Based on Multi-Timescales

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