scholarly journals Effective temperature of self-similar time series

2005 ◽  
Vol 8 (4) ◽  
pp. 761
Author(s):  
Olemskoi ◽  
Kokhan
Keyword(s):  
Time Series ◽  
Effective Temperature ◽  
Similar Time ◽  
Self Similar

scholarly journals A NOVEL R/S FRACTAL ANALYSIS AND WAVELET ENTROPY CHARACTERIZATION APPROACH FOR ROBUST FORECASTING BASED ON SELF-SIMILAR TIME SERIES MODELING

Fractals ◽  
2020 ◽  
Vol 28 (08) ◽  
pp. 2040032
Author(s):  
YELIZ KARACA ◽  
DUMITRU BALEANU
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Fractal Analysis ◽  
Back Propagation ◽  
Long Term Memory ◽  
Similar Time ◽  
Wavelet Entropy ◽  
Feed Forward Back Propagation ◽  
Self Similar

It has become vital to effectively characterize the self-similar and regular patterns in time series marked by short-term and long-term memory in various fields in the ever-changing and complex global landscape. Within this framework, attempting to find solutions with adaptive mathematical models emerges as a major endeavor in economics whose complex systems and structures are generally volatile, vulnerable and vague. Thus, analysis of the dynamics of occurrence of time section accurately, efficiently and timely is at the forefront to perform forecasting of volatile states of an economic environment which is a complex system in itself since it includes interrelated elements interacting with one another. To manage data selection effectively and attain robust prediction, characterizing complexity and self-similarity is critical in financial decision-making. Our study aims to obtain analyzes based on two main approaches proposed related to seven recognized indexes belonging to prominent countries (DJI, FCHI, GDAXI, GSPC, GSTPE, N225 and Bitcoin index). The first approach includes the employment of Hurst exponent (HE) as calculated by Rescaled Range ([Formula: see text]) fractal analysis and Wavelet Entropy (WE) in order to enhance the prediction accuracy in the long-term trend in the financial markets. The second approach includes Artificial Neural Network (ANN) algorithms application Feed forward back propagation (FFBP), Cascade Forward Back Propagation (CFBP) and Learning Vector Quantization (LVQ) algorithm for forecasting purposes. The following steps have been administered for the two aforementioned approaches: (i) HE and WE were applied. Consequently, new indicators were calculated for each index. By obtaining the indicators, the new dataset was formed and normalized by min-max normalization method’ (ii) to form the forecasting model, ANN algorithms were applied on the datasets. Based on the experimental results, it has been demonstrated that the new dataset comprised of the HE and WE indicators had a critical and determining direction with a more accurate level of forecasting modeling by the ANN algorithms. Consequently, the proposed novel method with multifarious methodology illustrates a new frontier, which could be employed in the broad field of various applied sciences to analyze pressing real-world problems and propose optimal solutions for critical decision-making processes in nonlinear, complex and dynamic environments.

scholarly journals GENERALIZED APPROACH TO HURST EXPONENT ESTIMATING BY TIME SERIES

2018 ◽  
Vol 8 (1) ◽  
pp. 28-31 ◽  
Author(s):  
Lyudmyla Kirichenko ◽  
Tamara Radivilova ◽  
Vitalii Bulakh
Keyword(s):  
Time Series ◽  
Fractal Analysis ◽  
Hurst Exponent ◽  
Hypothesis Test ◽  
Interval Estimation ◽  
Stationary Time Series ◽  
Similar Time ◽  
Self Similar ◽  
Short Time

This paper presents a generalized approach to the fractal analysis of self-similar random processes by short time series. Several stages of the fractal analysis are proposed. Preliminary time series analysis includes the removal of short-term dependence, the identification of true long-term dependence and hypothesis test on the existence of a self-similarity property. Methods of unbiased interval estimation of the Hurst exponent in cases of stationary and non-stationary time series are discussed. Methods of estimate refinement are proposed. This approach is applicable to the study of self-similar time series of different nature.

Properties of wavelet coefficients of self-similar time series

2015 ◽  
Vol 6 (1) ◽  
pp. 1492-1499 ◽  
Author(s):  
Vyacheslav V. Lyashenko ◽  
Zh. V. Deineko ◽  
M. Ayaz Ahmad
Keyword(s):  
Time Series ◽  
Wavelet Coefficients ◽  
Similar Time ◽  
Self Similar

scholarly journals A Study of Wavelet Analysis and Data Extraction from Second-Order Self-Similar Time Series

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Leopoldo Estrada Vargas ◽  
Deni Torres Roman ◽  
Homero Toral Cruz
Keyword(s):  
Time Series ◽  
Discrete Time ◽  
Close Association ◽  
Second Order ◽  
Basis Functions ◽  
Hurst Index ◽  
Similar Time ◽  
Sample Mean ◽  
Discrete Time Series ◽  
Self Similar

Statistical analysis and synthesis of self-similar discrete time signals are presented. The analysis equation is formally defined through a special family of basis functions of which the simplest case matches the Haar wavelet. The original discrete time series is synthesized without loss by a linear combination of the basis functions after some scaling, displacement, and phase shift. The decomposition is then used to synthesize a new second-order self-similar signal with a different Hurst index than the original. The components are also used to describe the behavior of the estimated mean and variance of self-similar discrete time series. It is shown that the sample mean, although it is unbiased, provides less information about the process mean as its Hurst index is higher. It is also demonstrated that the classical variance estimator is biased and that the widely accepted aggregated variance-based estimator of the Hurst index results biased not due to its nature (which is being unbiased and has minimal variance) but to flaws in its implementation. Using the proposed decomposition, the correct estimation of theVariance Plotis described, as well as its close association with the popularLogscale Diagram.

scholarly journals Changes in the Occurrence of Late Spring Frost in Poland

Agronomy ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1835
Author(s):  
Dariusz Graczyk ◽  
Małgorzata Szwed
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Growing Season ◽  
Significant Trend ◽  
Similar Time ◽  
Spring Frost ◽  
Late Spring Frost ◽  
Low Probability ◽  
Spring Frosts ◽  
Agricultural Regions

Trends in the appearance of the last spring frost for three thresholds of minimum daily air temperature at the height of 2 m and near the ground were examined for six meteorological stations located in two agricultural regions in Poland. For most time series, the last spring frost, calculated as a consecutive day of the year, showed a statistically significant trend indicating its earlier appearance from 1.6 to about 3.5 days per decade. The date of the last spring frost was also calculated in relation to the ongoing growing season. In this case, few statistically significant changes in the dates of the last frosts were found. The probability of the last spring frost on a specific day of the calendar year and the day of the growing season was also examined for two periods: 1961–1990 and 1991–2020. For low probability levels corresponding to the early dates of the last spring frost, the last frost usually occurred much earlier (6–14 days) in 1991–2020. With the probability levels of 80–90% describing the late occurrence of the last frost with a frequency of once every 5–10 years, at some stations, the last spring frosts occurred at a similar time for both periods.

A self-adapting multi-LSTM ensemble regression mode for failure prediction of transmission line network from wireless mesh nodes’ data

2020 ◽  
pp. 1-9
Author(s):  
Hongbin Sun ◽  
Mingjun Liu ◽  
Zhejun Qing ◽  
Chandler Miller
Keyword(s):  
Time Series ◽  
Transmission Line ◽  
Time Series Data ◽  
Failure Prediction ◽  
Series Data ◽  
Good Prediction ◽  
Similar Time ◽  
Prediction Ability ◽  
Wireless Mesh ◽  
Ensemble Regression

Transmission lines’ condition monitoring is an important part of smart grid construction. To ensure fast and efficient transmission of data, many mash-based wireless networks devices are adopted to collect status information. Since these nodes are exposed to the natural environment, vulnerable to damage, so it is very necessary to be predicting nodes’ fault. However, these mesh nodes are affected by a variety of complex and time-series factors, and traditional models are difficult to achieve effective failure prediction. To solve this problem, this paper proposes a self-adapting multi-LSTM ensemble regression model for transmission line network’s wireless mesh node failure prediction (MLSTM-FP), through establishes the corresponding relationship between similar time factors and LSTMs, the proposed model can realize multi time series data self-adapting and accurate failure prediction of transmission line network’s wireless mesh nodes, The experimental results show that the proposed method has a good prediction ability than traditional methods.

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