scholarly journals Long-Range Correlations and Characterization of Financial and Volcanic Time Series

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 441 ◽  
Author(s):  
Maria C. Mariani ◽  
Peter K. Asante ◽  
Md Al Masum Bhuiyan ◽  
Maria P. Beccar-Varela ◽  
Sebastian Jaroszewicz ◽  
...  
Keyword(s):  
Time Series ◽  
Long Range ◽  
Time Series Data ◽  
Financial Time Series ◽  
Seismic Station ◽  
Volcanic Eruptions ◽  
Series Data ◽  
Fluctuation Analysis ◽  
Scaling Properties ◽  
Long Range Correlations

In this study, we use the Diffusion Entropy Analysis (DEA) to analyze and detect the scaling properties of time series from both emerging and well established markets as well as volcanic eruptions recorded by a seismic station, both financial and volcanic time series data have high frequencies. The objective is to determine whether they follow a Gaussian or Lévy distribution, as well as establish the existence of long-range correlations in these time series. The results obtained from the DEA technique are compared with the Hurst R/S analysis and Detrended Fluctuation Analysis (DFA) methodologies. We conclude that these methodologies are effective in classifying the high frequency financial indices and volcanic eruption data—the financial time series can be characterized by a Lévy walk while the volcanic time series is characterized by a Lévy flight.

The MDF technique for the analysis of tokamak edge plasma fluctuations

2013 ◽  
Vol 80 (1) ◽  
pp. 43-58 ◽  
Author(s):  
M. Lafouti ◽  
M. Ghoranneviss ◽  
S. Meshkani ◽  
A. Salar Elahi
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Multifractal Spectrum ◽  
Edge Plasma ◽  
Series Data ◽  
Fluctuation Analysis ◽  
Plasma Edge ◽  
Langmuir Probes ◽  
Long Range Correlations ◽  
Tokamak Edge

Tokamak edge plasma was analyzed by applying the multifractal detrend fluctuation analysis (MF-DFA) technique. This method has found wide application in the analysis of correlations and characterization of scaling behavior of the time-series data in physiology, finance, and natural sciences. The time evolution of the ion saturation current (Is), the floating potential fluctuation (Vf), the poloidal electric field (Ep), and the radial particle flux (Γr) has been measured by using a set of Langmuir probes consisting of four tips on the probe head. The generalized Hurst exponents (h(q)), local fluctuation function (Fq(s)), the Rényi exponents (τ(q)) as well as the multifractal spectrum f(αh) have been calculated by applying the MF-DFA method to Is, Vf, and the magnetohydrodynamic (MHD) fluctuation signal. Furthermore, we perform the shuffling and the phase randomization techniques to detect the sources of multifractality. The nonlinearity shape of τ(q) reveals a multifractal behavior of the time-series data. The results show that in the presence of biasing, Is, Vf, Ep, and Γr reduce about 25%, 90%, 70%, and 50%, respectively, compared with the situation with no biasing. Also, they reduce about 15%, 90%, 35%, and 25%, respectively, after resonant helical magnetic field (RHF) application. In the presence of biasing or RHF, the amplitude of the power spectrum of Is, Vf, Γr, and MHD activity reduce remarkably in all the ranges of frequency, while their h(q) increase. The values of h(q) have been restricted between 0.6 and 0.68. These results are evidence of the existence of long-range correlations in the plasma edge turbulence. They also show the self-similar nature of the plasma edge fluctuations. Biasing or RHF reduces the amount of Fq(s). The multifractal spectrum width of Is, Vf, and MHD fluctuation amplitude reduce about 60%, 70%, and 42%, respectively, by applying biasing. In the presence of RHF, their width reduces about 60%, 85%, and 75%, respectively. It means that biasing and RHF reduce the degree of multifractality.

scholarly journals DETRENDED FLUCTUATION ANALYSIS OF THE TCP-RED ALGORITHM

2009 ◽  
Vol 19 (12) ◽  
pp. 4237-4245 ◽  
Author(s):  
XI CHEN ◽  
SIU-CHUNG WONG ◽  
CHI K. TSE ◽  
LJILJANA TRAJKOVIĆ
Keyword(s):  
Time Series ◽  
Long Range ◽  
Power Law ◽  
Detrended Fluctuation Analysis ◽  
Time Series Data ◽  
Analytical Models ◽  
Series Data ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
Detrended Fluctuation

It has been observed that Internet gateways employing Transport Control Protocol (TCP) and the Random Early Detection (RED) control algorithm may exhibit instability and oscillatory behavior. Most control methods proposed in the past have been based on analytical models that rely on statistical measurements of network parameters. In this paper, we apply the detrended fluctuation analysis (DFA) method to analyze stability of the TCP-RED system. The DFA is used to analyze time-series data and generate power-law scaling exponents, which indicate the long-range correlations of the time series. We quantify the stability of the TCP-RED system by examining the variation of the DFA power-law scaling exponent when the system parameters are varied. We also study the long-range power-law correlations of TCP window periods.

scholarly journals Long-range correlation and nonlinearity in geochemical time series of gas discharges from Mt. Etna, and changes with 2001 and 2002–2003 eruptions

2010 ◽  
Vol 17 (6) ◽  
pp. 733-751 ◽  
Author(s):  
A. Paonita
Keyword(s):  
Time Series ◽  
Long Range ◽  
Time Series Data ◽  
Flicker Noise ◽  
Series Data ◽  
Scaling Exponent ◽  
Time Reversibility ◽  
Long Range Correlations ◽  
Range Correlation ◽  
Mt Etna

Abstract. In this paper, spectral and detrended fluctuation analyses, as well as time reversibility and magnitude-sign decomposition, have been applied to the 10-year time-series data resulting from geochemical monitoring of gas emissions on the flanks of Mt. Etna, and gases from a CO2 exploitation well located tens of kilometers from the volcano. The analysis of the time series which showed main effects of fractionation between gases due to selective dissolution in aquifers (e.g., the CO2 concentration series), revealed the occurrence of random fluctuations in time, typical of systems where several processes combine linearly. In contrast, the series of He isotopic composition exhibited power-law behavior of the second-order fluctuation statistics, with values of the scaling exponent close to 0.9. When related to the spectral exponent, this value indicates that the isotopic series closely resemble fractal flicker-noise signals having persistent long-range correlations. The isotopic signals also displayed asymmetry under time reversal and long-range correlation of the associated magnitude series, therefore it was statistically proved the presence of nonlinearity. Both long-range correlation and nonlinearity in time series have been generally considered as distinctive features of dynamic systems where numerous processes interact by feedback mechanisms, in accordance with the paradigm of self-organized criticality (SOC). Thus, it is here proposed that the system that generated the isotope series worked under conditions of SOC. Since the fluctuations of the isotope series have been related to magma degassing, the previous results place constraints on the dynamics of such process, and suggest that nonequilibrium conditions must be dominant. It remains unclear whether the signature of SOC is directly due to volatile degassing from magma, or if it derives from the interaction between melt and the stress field, which certainly influences magma decompression. The strength of scaling appears to increase after 2002 (α values from 0.8 up to 1.2), focusing on transition of the Etnean system from typical SOC toward conditions of lower criticality. By comparing this transition with those of geophysical observables, it can be suggested that the drop in the rate of magma supply, subsequent to the paroxysms of 2001 and 2002–2003, was the main cause of the scaling change.

scholarly journals An Application of the Associate Hopfield Network for Pattern Matching in Chart Analysis

2021 ◽  
Vol 11 (9) ◽  
pp. 3876
Author(s):  
Weiming Mai ◽  
Raymond S. T. Lee
Keyword(s):  
Time Series ◽  
Pattern Matching ◽  
Time Series Data ◽  
Financial Time Series ◽  
Noisy Data ◽  
Hopfield Neural Network ◽  
Hopfield Network ◽  
Series Data ◽  
Financial Time ◽  
Chart Patterns

Chart patterns are significant for financial market behavior analysis. Lots of approaches have been proposed to detect specific patterns in financial time series data, most of them can be categorized as distance-based or training-based. In this paper, we applied a trainable continuous Hopfield Neural Network for financial time series pattern matching. The Perceptually Important Points (PIP) segmentation method is used as the data preprocessing procedure to reduce the fluctuation. We conducted a synthetic data experiment on both high-level noisy data and low-level noisy data. The result shows that our proposed method outperforms the Template Based (TB) and Euclidean Distance (ED) and has an advantage over Dynamic Time Warping (DTW) in terms of the processing time. That indicates the Hopfield network has a potential advantage over other distance-based matching methods.

scholarly journals Investigating Long-Range Dependence of Emerging Asian Stock Markets Using Multifractal Detrended Fluctuation Analysis

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1157
Author(s):  
Faheem Aslam ◽  
Saima Latif ◽  
Paulo Ferreira
Keyword(s):  
Time Series ◽  
Long Range ◽  
Stock Markets ◽  
Detrended Fluctuation Analysis ◽  
Financial Time Series ◽  
Long Range Dependence ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Financial Time ◽  
Detrended Fluctuation

The use of multifractal approaches has been growing because of the capacity of these tools to analyze complex properties and possible nonlinear structures such as those in financial time series. This paper analyzes the presence of long-range dependence and multifractal parameters in the stock indices of nine MSCI emerging Asian economies. Multifractal Detrended Fluctuation Analysis (MFDFA) is used, with prior application of the Seasonal and Trend Decomposition using the Loess (STL) method for more reliable results, as STL separates different components of the time series and removes seasonal oscillations. We find a varying degree of multifractality in all the markets considered, implying that they exhibit long-range correlations, which could be related to verification of the fractal market hypothesis. The evidence of multifractality reveals symmetry in the variation trends of the multifractal spectrum parameters of financial time series, which could be useful to develop portfolio management. Based on the degree of multifractality, the Chinese and South Korean markets exhibit the least long-range dependence, followed by Pakistan, Indonesia, and Thailand. On the contrary, the Indian and Malaysian stock markets are found to have the highest level of dependence. This evidence could be related to possible market inefficiencies, implying the possibility of institutional investors using active trading strategies in order to make their portfolios more profitable.

scholarly journals Multiband Prediction Model for Financial Time Series with Multivariate Empirical Mode Decomposition

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Md. Rabiul Islam ◽  
Md. Rashed-Al-Mahfuz ◽  
Shamim Ahmad ◽  
Md. Khademul Islam Molla
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Time Series Data ◽  
Financial Time Series ◽  
Arma Model ◽  
Series Data ◽  
Discrete Wavelet ◽  
Multivariate Empirical Mode Decomposition ◽  
Financial Time ◽  
Mode Decomposition

This paper presents a subband approach to financial time series prediction. Multivariate empirical mode decomposition (MEMD) is employed here for multiband representation of multichannel financial time series together. Autoregressive moving average (ARMA) model is used in prediction of individual subband of any time series data. Then all the predicted subband signals are summed up to obtain the overall prediction. The ARMA model works better for stationary signal. With multiband representation, each subband becomes a band-limited (narrow band) signal and hence better prediction is achieved. The performance of the proposed MEMD-ARMA model is compared with classical EMD, discrete wavelet transform (DWT), and with full band ARMA model in terms of signal-to-noise ratio (SNR) and mean square error (MSE) between the original and predicted time series. The simulation results show that the MEMD-ARMA-based method performs better than the other methods.

Modeling and forecasting multifractal wavelet scale: Western market versus eastern market

2018 ◽  
Vol 29 (11) ◽  
pp. 1850109 ◽  
Author(s):  
Emrah Oral ◽  
Gazanfer Unal
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Price Change ◽  
Wavelet Coherence ◽  
Series Data ◽  
Primary Study ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Time Frequency

This leading primary study is about modeling multifractal wavelet scale time series data using multiple wavelet coherence (MWC), continuous wavelet transform (CWT) and multifractal detrended fluctuation analysis (MFDFA) and forecasting with vector autoregressive fractionally integrated moving average (VARFIMA) model. The data is acquired from Yahoo Finances!, which is composed of 1671 daily stock market of eastern (NIKKEI, TAIEX, KOPSI) and western (SP500, FTSE, DAX) markets. Once the co-movement dependencies on time-frequency space are determined with MWC, the coherent data is extracted out of raw data at a certain scale by using CWT. The multifractal behavior of the extracted series is verified by MFDFA and its local Hurst exponents have been calculated obtaining root mean square of residuals at each scale. This inter-calculated fluctuation function time series has been re-scaled and used to estimate the process with VARFIMA model and forecasted accordingly. The results have shown that the direction of price change is determined without difficulty and the efficiency of forecasting has been substantially increased using highly correlated multifractal wavelet scale time series data.

scholarly journals Time-Scaling Properties of Sunshine Duration Based on Detrended Fluctuation Analysis over China

Atmosphere ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 83 ◽  
Author(s):  
Lei Jiang ◽  
Jiping Zhang ◽  
Yan Fang
Keyword(s):  
Time Series ◽  
Detrended Fluctuation Analysis ◽  
Sunshine Duration ◽  
Underlying Surface ◽  
Fluctuation Analysis ◽  
Scaling Properties ◽  
Long Range Correlations ◽  
Time Scaling ◽  
Daily Sunshine ◽  
Detrended Fluctuation

The spatial and temporal variabilities of the daily Sunshine Duration (SSD) time series from the Chinese Meteorological Administration during the 1954–2009 period are examined by the Detrended Fluctuation Analysis (DFA) method. As a whole, weak long-range correlations (LRCs) are found in the daily SSD anomaly records over China. LRCs are also verified by shuffling the SSD records. The proportion of the stations with LRCs accounts for about 97% of the total. Many factors affect the scaling properties of the daily SSD records such as sea-land difference and Tibetan Plateau landform and so on. We find land use and land cover as one of the important factors closely links to LRCs of the SSD. Strong LRCs of the SSD mainly happen in underlying surface of deserts and crops, while weak LRCs occur in forest and grassland. Further studies of scaling behaviors are still necessary to be performed due to the complex underlying surface and climate system.

MODELING MEMORY OF ECONOMIC AND FINANCIAL TIME SERIES

2005 ◽  
Vol 50 (01) ◽  
pp. 1-8 ◽  
Author(s):  
PETER M. ROBINSON
Keyword(s):  
Time Series ◽  
Statistical Modeling ◽  
Statistical Models ◽  
Time Series Data ◽  
Financial Time Series ◽  
Nonlinear Models ◽  
Series Data ◽  
Financial Variables ◽  
Financial Time ◽  
Linear And Nonlinear

Much time series data are recorded on economic and financial variables. Statistical modeling of such data is now very well developed, and has applications in forecasting. We review a variety of statistical models from the viewpoint of "memory", or strength of dependence across time, which is a helpful discriminator between different phenomena of interest. Both linear and nonlinear models are discussed.

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