Cross-sample entropy estimation for time series analysis: a nonparametric approach

Ramírez-Parietti, Ignacio; Contreras-Reyes, Javier E.; Idrovo-Aguirre, Byron J.

doi:10.1007/s11071-021-06759-8

Cited by 20 publications

(6 citation statements)

References 37 publications

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“…Correntropy not only has inferential properties that could be used for neural network analysis, but another approach could be, for example, cross-sample entropy-based techniques. One such direction was shown to be effective in [41] with reported results of simulation on exchange market datasets.…”

Section: Discussionmentioning

confidence: 99%

Correntropy-Based Constructive One Hidden Layer Neural Network

Nayyeri,

Rouhani,

Yazdi

et al. 2024

Algorithms

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One of the main disadvantages of the traditional mean square error (MSE)-based constructive networks is their poor performance in the presence of non-Gaussian noises. In this paper, we propose a new incremental constructive network based on the correntropy objective function (correntropy-based constructive neural network (C2N2)), which is robust to non-Gaussian noises. In the proposed learning method, input and output side optimizations are separated. It is proved theoretically that the new hidden node, which is obtained from the input side optimization problem, is not orthogonal to the residual error function. Regarding this fact, it is proved that the correntropy of the residual error converges to its optimum value. During the training process, the weighted linear least square problem is iteratively applied to update the parameters of the newly added node. Experiments on both synthetic and benchmark datasets demonstrate the robustness of the proposed method in comparison with the MSE-based constructive network, the radial basis function (RBF) network. Moreover, the proposed method outperforms other robust learning methods including the cascade correntropy network (CCOEN), Multi-Layer Perceptron based on the Minimum Error Entropy objective function (MLPMEE), Multi-Layer Perceptron based on the correntropy objective function (MLPMCC) and the Robust Least Square Support Vector Machine (RLS-SVM).

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Section: Discussionmentioning

confidence: 99%

Correntropy-Based Constructive One Hidden Layer Neural Network

Nayyeri,

Rouhani,

Yazdi

et al. 2024

Algorithms

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“…Further research could involve implementing a cross-sample entropy for the synchronization of the BCI and CPI indexes (Ramírez-Parietti et al 2021). With respect to the proposed models, further studies might focus on other indexes, such as a daily S&P500 index, the Hang-Seng index (Shum 2020), foreign exchange rates (Ramírez-Parietti et al 2021), or another time series. Moreover, economic uncertainty could be modeled by a multivariate approach (Arnold and Günther 2001;Contreras-Reyes 2022).…”

Section: Discussionmentioning

confidence: 99%

“…Note that α 1 and α 2 were not significant for a 95% confidence level, indicating that the CPI time series do not require a threshold component. Therefore, the CPI time series could be fitted by the simplest model, such as the ARMA(1, 1)-GARCH(0, 2) one (Ramírez-Parietti et al 2021). (Ljung and Box 1978) was considered for several lags to check if the residuals of the fitted models are white noise.…”

Section: Arma-tgarch Modelsmentioning

confidence: 99%

A Threshold GARCH Model for Chilean Economic Uncertainty

Chávez

Contreras-Reyes

Idrovo-Aguirre

2022

JRFM

Self Cite

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In this paper, an autoregressive moving average (ARMA) model with threshold generalized autoregressive conditional heteroscedasticity (TGARCH) innovations is considered to model Chilean economic uncertainty time series. Uncertainty is measured through the Business Confidence Index (BCI) and Consumer Perception Index (CPI). The BCI time series provide useful information about industry; commerce; the finance, mining, construction, and agricultural sectors; and the global economic situation and the general business situation. As a counterpart, the CPI time series measure the perception of consumers regarding the state of the Chilean economy, evaluating their economic situation and expectations. The ARMA-TGARCH model is compared with the classical seasonal ARIMA and threshold AR ones. The results show that the ARMA-TGARCH model explains the regime changes in economic uncertainty better than the others, given that negative shocks are associated with statistically significant and quantitatively larger levels of volatility produced by the COVID-19 pandemic. In addition, a diagnostic analysis and prediction performance illustrates the suitability of the proposed model. Using a cross-validation analysis for the forecasting performance, a proposed heteroscedastic model may effectively help improve the forecasting accuracy for observations related to pessimism periods like the social uprising and the COVID-19 crisis which produced volatility in the Chilean uncertainty indexes.

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“…They also concluded that the cryptocurrency fluctuations affected more than the equity markets. Ramírez-Parietti et al . (2021) considered the cross-sample entropy (CSE) estimation under a nonparametric approach and gave a general estimation framework.…”

Section: Literature Reviewmentioning

confidence: 99%

High short interest stocks performance during the Covid-19 crisis: an informational efficacy measure based on permutation-entropy approach

Siokis

2023

JES

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PurposeThe author examine the performance of a number of high short interest stocks along with the prices of the GameStop stock and three major stock exchange indices, particularly for the period after the eruption of the Covid-19 crisis.Design/methodology/approachWith the employment of the complexity–entropy causality plane approach, the author categorize the stock prices in terms of the level of informational efficiency.FindingsThe author reported that the efficiency level for the index of the high short interest stocks falls considerably, not only at the onset of the Covid-19 crisis but during the health crisis period at hand. This is translated into proof of less uncertainty in predicting the stock prices of these specific stocks. On the other hand, the GameStop prices exhibit the same behavior as those with the high short interest firms, but change considerably in the middle of the crisis. The reversal of the behavior, by obtaining higher informational efficiency levels, is attributed to the short squeeze frenzy that increased the price of the stock many times over. Among the stock market indices, the Dow Jones Industrial Average and the S&P 500 decreased their efficiency levels marginally, after the surge of the crisis, while the Russell 2000 index kept the level intact. The high and stable degree of randomness could be attributed to the measures taken concurrently by the Federal Reserve and the government immediately after the outbreak of the crisis.Originality/valueThis is one of the few studies that examine the impact of short selling behavior on the efficiency level of certain stocks' prices, particularly during the health public crisis. It provides an alternative approach to measuring quantitatively the degree of inefficiency and randomness.

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Cross-sample entropy estimation for time series analysis: a nonparametric approach

Cited by 20 publications

References 37 publications

Correntropy-Based Constructive One Hidden Layer Neural Network

Correntropy-Based Constructive One Hidden Layer Neural Network

A Threshold GARCH Model for Chilean Economic Uncertainty

High short interest stocks performance during the Covid-19 crisis: an informational efficacy measure based on permutation-entropy approach

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