Information measure for financial time series: Quantifying short-term market heterogeneity

Ponta, Linda; Carbone, Anna Filomena

doi:10.1016/j.physa.2018.06.085

Cited by 25 publications

(23 citation statements)

References 50 publications

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“…The convexity of divergence measure J n (A, B) is an additional attractive feature of the Shannon entropy H(G) as a measure of diversity of a distribution. The application of measure of diversity is discussed in [19,20]. In this study, we assume that the Jensen difference Equation 3arising from a generalized class of entropy functions including the exponential entropy due to Pal and Pal [21], which is called an exponential J-divergence, and examine its convexity.…”

Section: Picture Fuzzy Divergence Measurementioning

confidence: 99%

Application of Exponential Jensen Picture Fuzzy Divergence Measure in Multi-Criteria Group Decision Making

et al. 2019

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A divergence measure plays a crucial part in discriminating two probability distributions and drawing inferences constructed on such discrimination. The intention of this study is to propose such a divergence measure based on Jensen inequality and exponential entropy in the settings of probability theory. Further, the idea has been generalized to fuzzy sets to familiarize a novel picture fuzzy divergence measure. Besides proposing the validity, some of its key properties are also deliberated. Finally, two illustrative examples are solved based on the proposed picture fuzzy divergence measure which shows the expediency and effectiveness of the proposed approach.

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Section: Picture Fuzzy Divergence Measurementioning

confidence: 99%

Application of Exponential Jensen Picture Fuzzy Divergence Measure in Multi-Criteria Group Decision Making

et al. 2019

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“…The partitioning of a relevant sequence into disjoint sets was usually performed based on the uniform division of the block sizes. The estimation of entropy for the subsequent partitions was performed for the different block sizes correspondingly [38,39].…”

Section: Feature Extraction Based On Shannon Entropy and Logarithmic mentioning

confidence: 99%

An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network

et al. 2018

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This paper proposes a new islanding detection technique based on the combination of a wavelet packet transform (WPT) and a probabilistic neural network (PNN) for grid-tied photovoltaic systems. The point of common coupling (PCC) voltage is measured and processed by the WPT to find the normalized Shannon entropy (NSE) and the normalized logarithmic energy entropy (NLEE). Subsequently, the yield feature vectors are fed to the PNN classifier to classify the disturbances. The PNN is trained with different spread factors to obtain better classification accuracy. For the best performance of the proposed method, the precise analysis is done for the selection of the type of input data for the PNN, the type of mother wavelet, and the required transform level which is based on the accuracy, simplicity, specificity, speed, and cost parameters. The results show that, by using normalized Shannon entropy and the normalized logarithmic energy entropy, not only it offers simplicity, specificity and reduced costs, it also has better accuracy compared to other smart and passive methods. Based on the results, the proposed islanding detection technique is highly accurate and does not mal-operate during islanding and non-islanding events.

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“…The entropy of a random variable indicates the required average amount of information to describe the random variable [33] which has been adopted in many studies [34,35]. The entropy of a discrete random variable X = (x 1 , x 2 , .…”

Section: The Optimal Selection Of Candidate Input Variables Using Mrmmentioning

confidence: 99%

Multi-Step Ahead Wind Power Generation Prediction Based on Hybrid Machine Learning Techniques

2018

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Accurate generation prediction at multiple time-steps is of paramount importance for reliable and economical operation of wind farms. This study proposed a novel algorithmic solution using various forms of machine learning techniques in a hybrid manner, including phase space reconstruction (PSR), input variable selection (IVS), K-means clustering and adaptive neuro-fuzzy inference system (ANFIS). The PSR technique transforms the historical time series into a set of phase-space variables combining with the numerical weather prediction (NWP) data to prepare candidate inputs. A minimal redundancy maximal relevance (mRMR) criterion based filtering approach is used to automatically select the optimal input variables for the multi-step ahead prediction. Then, the input instances are divided into a set of subsets using the K-means clustering to train the ANFIS. The ANFIS parameters are further optimized to improve the prediction performance by the use of particle swarm optimization (PSO) algorithm. The proposed solution is extensively evaluated through case studies of two realistic wind farms and the numerical results clearly confirm its effectiveness and improved prediction accuracy compared to benchmark solutions.

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Information measure for financial time series: Quantifying short-term market heterogeneity

Cited by 25 publications

References 50 publications

Application of Exponential Jensen Picture Fuzzy Divergence Measure in Multi-Criteria Group Decision Making

Application of Exponential Jensen Picture Fuzzy Divergence Measure in Multi-Criteria Group Decision Making

An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network

Multi-Step Ahead Wind Power Generation Prediction Based on Hybrid Machine Learning Techniques

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