Narrow Results

This study aims to examine the interaction among tourism revenue (TOV), the real exchange rate (REX), and economic development in Vietnam throughout 1995–2019. Using the bivariate and multivariate wavelet frameworks, we examine the lead–lag connectedness, co-movement and dynamic associations between these indicators across various time and frequency domains. By doing so, we employ wavelet transform coherence (WTC), cross-wavelet transform (XWT), partial wavelet coherence (PWC) and multiple wavelet coherence (MWC) frameworks. The findings indicate low covariance but a positive and robust nexus between tourism demand (TOV), economic growth (gross domestic product (GDP)), and the REX in the time–frequency space. In the long run, interdependence between variables is primarily negative and weak. The outcomes of PWC and MWC reveal that REX and GDP determinants affect the TOV–GDP and TOV–REX relationships under different frequencies, respectively. These results are of interest and significance to the Vietnamese government and policymakers as the outcomes have important implications for informing their decision-making.

The correlation of carbon price and coal price is a hot issue in the domestic and international academic communities. By performing cross-wavelet analysis on the two time series, extracting the energy spectrum, condensed spectrum and phase spectrum and displaying the relevant details of the two signals in the time–frequency domain at multiple scales, the mutual influence of the two signals can be accurately distinguished at multiple periods and scales. The experimental results show that: (1) the two signals have a statistically significant correlation; (2) the carbon price is positively correlated with the coal price in the low-frequency domain, with the coal price leading the carbon price; and (3) the carbon price is negatively correlated with the coal price in the high-frequency domain for a given period, with the carbon price leading the domestic coal price and lagging the imported coal price. The experimental results support the results of our theoretical analysis both quantitatively and qualitatively.

Dynamics of complex systems is studied by first considering a chaotic time series generated by Lorenz equations and adding noise to it. The trend (smooth behavior) is separated from fluctuations at different scales using wavelet analysis and a prediction method proposed by Lorenz is applied to make out of sample predictions at different regions of the time series. The prediction capability of this method is studied by considering several improvements over this method. We then apply this approach to a real financial time series. The smooth time series is modeled using techniques of non linear dynamics. Our results for predictions suggest that the modified Lorenz method gives better predictions compared to those from the original Lorenz method. Fluctuations are analyzed using probabilistic considerations.

This paper proposes new perspective on the nexus between transportation and urbanization in China to test the search-matching theory. We find that the linkage between transportation and urbanization has both frequency and time-varying features. We find that transportation improves urbanization in the short term, while urbanization plays the importation role in transportation during the period 1969–1996. This result obviously supports search-matching theory that in the subsample periods, the transportation infrastructure exerts positive effects on urbanization in the short term but not in the long term. In the long term, urbanization will promote the development of transportation, while short-term traffic infrastructure investment can effectively improve the transfer of population to urban regions. It would be beneficial for the government to formulate the scientific traffic planning policy and adjust the transport structure to improve urbanization.

In this paper, we examine the differences between CNY and other major currencies in coherence and the lead–lag relationship across the different time horizons to clarify whether crude oil, monetary factors, or both drive the movement of exchange rates. We employ partial and multiple wavelet coherence analyses to examine oil-exchange co-movement by excluding the influence of Federal Reserve System (FED) monetary policy — namely, the stance and uncertainty of monetary policy — and the difference in domestic and foreign monetary policy rates. Overall, we find that monetary easing by the FED is a major factor driving the co-movement. Specifically, after excluding the possible effects of monetary policy factors, the movement of the Euro exhibits the strongest and the Japanese yen the weakest dependence on crude oil price changes, whereas the British pound shows a moderate dependence. By contrast, the CNY shows strong co-movement with the crude oil price only over the long term implying the low degree of integration with the global markets. Our empirical results provide meaningful information for investors and policymakers.

Experimental data on atmospheric muons obtained with Russian-Italian coordinate detector DECOR have been processed by means of method of wavelet transformation. Results of analysis exhibit time perturbations caused by effects like Forbush decreases and allow to determine not only their periods but also moments of their appearance.

Two mathematical methods, the Fourier and wavelet transforms, were used to study the short term cardiovascular control system. Time series, picked from electrocardiogram and arterial blood pressure lasting 6 minutes, were analyzed in supine position (SUP), during the first (HD1) and the second parts (HD2) of 90° head down tilt, and during recovery (REC). The wavelet transform was performed using the Haar function of period to obtain wavelet coefficients. Power spectra components were analyzed within three bands, VLF (0.003–0.04), LF (0.04–0.15) and HF (0.15–0.4) with the frequency unit cycle/interval. Wavelet transform demonstrated a higher discrimination among all analyzed periods than the Fourier transform. For the Fourier analysis, the LF of R–R intervals and VLF of systolic blood pressure show more evident difference for different body positions. For the wavelet analysis, the systolic blood pressures show much more evident differences than the R–R intervals. This study suggests a difference in the response of the vessels and the heart to different body positions. The partial dissociation between VLF and LF results is a physiologically relevant finding of this work.

Moldy cores in apples are not initially obvious from the outside of the fruit, so developing methods to detect moldy cores is an important area of research in the apple industry. The objective of this study was to improve the ability of near-infrared spectrometry to detect moldy cores in apples. Transmission spectra were recorded for 200 apple samples in the range of 200–1100nm, and 140 and 60 samples were randomly selected as training and test sets, respectively. Signal de-noising was performed by wavelet thresholding based on the results of orthogonal experiments. The best wavelengths for discriminating between healthy and diseased apples were selected by a successive projection algorithm (SPA). The extracted wavelengths were used as the input in a back propagation artificial neural network (BP-ANN). Through these experiments, this study compared the correct recognition rates using different ratios of training to test numbers in the model, and functions in the hidden and output layers of the BP-ANN. The proposed method achieved the highest accuracies of 95.00% and 95.71% for the test and training sets, respectively. This method could be used to develop a portable instrument for detecting moldy cores in apples.

The present report describes the dynamic foundations of long-standing experimental work in the field of oscillatory dynamics in the human and animal brain. It aims to show the role of multiple oscillations in the integrative brain function, memory, and complex perception by a recently introduced conceptional framework: the super-synergy in the whole brain. Results of recent experiments related to the percept of the grandmother-face support our concept of super-synergy in the whole brain in order to explain manifestation of Gestalts and Memory-Stages. This report may also provide new research avenues in macrodynamics of the brain.

The chaotic van der Pol oscillator is a powerful tool for detecting defects in electric systems by using online partial discharge (PD) monitoring. This paper focuses on realizing weak PD signal detection in the strong periodic narrowband interference by using high sensitivity to the periodic narrowband interference signals and immunity to white noise and PD signals of chaotic systems. A new approach to removing the periodic narrowband interference by using a van der Pol chaotic oscillator is described by analyzing the motion characteristic of the chaotic oscillator on the basis of the van der Pol equation. Furthermore, the Floquet index for measuring the amplitude of periodic narrowband signals is redefined. The denoising signal processed by the chaotic van der Pol oscillators is further processed by wavelet analysis. Finally, the denoising results verify that the periodic narrowband and white noise interference can be removed efficiently by combining the theory of the chaotic van der Pol oscillator and wavelet analysis.

The combustion instabilities in a lean-burn natural gas engine have been studied. Using statistical analysis, phase-space reconstruction, and wavelet transforms, the effect of port gas injection on the dynamics of the indicated mean effective pressure (IMEP) fluctuations have been examined at a speed of 800rpm and engine load rates of 25% and 50%. The excessive air coefficient is 1.6 for each engine load, and the port gas injection timing (PGIT) ranges from 1 to 120 degrees of crankshaft angle (${}^{\circ }$CA) after top dead center (ATDC) of the intake process. The results show that the PGIT has a significant effect on cyclic combustion fluctuations and the dynamics of the combustion system for all studied engine loads. An unreasonable PGIT leads to increased combustion fluctuations, and loosened and bifurcated structures of combustion system attractors. Furthermore, for both low and medium engine loads, the IMEP time series at earlier gas injections ($\mathrm{\text{PGIT}}=1^{\circ }$CA and $30^{\circ }$CA ATDC) undergoes low-frequency fluctuation together with high-frequency fluctuations in an intermittent fashion. For other PGITs, high-frequency intermittent fluctuations become persistent combined with weak low-frequency oscillations. Our results can be used to understand the oscillation characteristics and the complex dynamics of combustion system in a lean-burn natural gas engine. In addition, they may also be beneficial to the development of more sophisticated engine control strategies.

Rabies remains a serious threat to public health in Asia, Africa and some parts of Europe with a case fatality rate of 95%. We adopted wavelet analysis to study the long-term recurrence of global rabies outbreaks and found that a 3- to 4-year periodicity has existed since 2005. Furthermore, a simple compartmental model is developed and analyzed to study the transmission dynamics, and to show the existence of the observed periodicity as well as the endemic feature of rabies among animals. Our findings indicate the existence of the oscillation patterns (recurrence), and the epidemic is at its peak since 2018.

Time series change point detection can identify the locations of abrupt points in many dynamic processes. It can help us to find anomaly data in an early stage. At the same time, detecting change points for long, periodic, and multiple input series data has received a lot of attention recently, and is universally applicable in many fields including power, environment, finance, and medicine. However, the performance of classical methods typically scales poorly for such time series.

In this paper, we propose CPMAN, a novel prediction-based change point detection approach via multi-stage attention networks. Our model consists of two key modules. First, in the time series prediction module, we employ the multi-stage attention-based networks and integrate the multi-series fusion mechanism. This module can adaptively extract features from the relevant input series and capture the long-term temporal dependencies. Secondly, in the change point detection module, we use the wavelet analysis-based algorithm to detect change points efficiently and identify the change points and outliers. Extensive experiments are conducted on various real-world datasets and synthetic datasets, proving the superiority and effectiveness of CPMAN. Our approach outperforms the state-of-the-art methods by up to 12.1% on the F1 Score.

A continuous wavelet analysis is performed for pattern recognition of the pseudorapidity density profile of singly charged particles produced in ${}^{16}$O+Ag/Br and ${}^{32}$S+Ag/Br interactions, each at an incident energy of $200$ GeV per nucleon in the laboratory system. The experiments are compared with a model prediction based on the ultra-relativistic quantum molecular dynamics (UrQMD). To eliminate the contribution coming from known source(s) of particle cluster formation like Bose–Einstein correlation (BEC), the UrQMD output is modified by “an algorithm that mimics the BEC as an after burner.” We observe that for both interactions particle clusters are found at same pseudorapidity locations at all scales. However, the cluster locations in the ${}^{16}$O+Ag/Br interaction are different from those found in the ${}^{32}$S+Ag/Br interaction. Significant differences between experiments and simulations are revealed in the wavelet pseudorapidity spectra that can be interpreted as the preferred pseudorapidity values and/or scales of the pseudorapidity interval at which clusters of particles are formed. The observed discrepancy between experiment and corresponding simulation should therefore be interpreted in terms of some kind of nontrivial dynamics of multiparticle production.

The propagation of a laser beam through turbulent media is modeled as a fractional Brownian motion (fBm). Time series corresponding to the center position of the laser spot (coordinates x and y) after traveling across air in turbulent motion, with different strength, are analyzed by the wavelet theory. Two quantifiers are calculated, the Hurst exponent, H, and the mean Normalized Total Wavelet Entropy, . It is verified that both quantifiers give complementary information about the turbulence.

In some applications, for instance, finance, biomechanics, turbulence or internet traffic, it is relevant to model data with a generalization of a fractional Brownian motion for which the Hurst parameter H is dependent on the frequency. In this contribution, we describe the multiscale fractional Brownian motions which present a parameter H as a piecewise constant function of the frequency. We provide the main properties of these processes: long-memory and smoothness of the paths. Then we propose a statistical method based on wavelet analysis to estimate the different parameters and prove a functional Central Limit Theorem satisfied by the empirical variance of the wavelet coefficients.

We consider a Gaussian time series, stationary or not, with long memory exponent d ∈ ℝ. The generalized spectral density function of the time series is characterized by d and by a function f*(λ) which specifies the short-range dependence structure. Our setting is semi-parametric in that both d and f* are unknown, and only the smoothness of f* around λ = 0 matters. The parameter d is the one of interest. It is estimated by regression using the wavelet coefficients of the time series, which are dependent when d ≠ 0. We establish a Central Limit Theorem (CLT) for the resulting estimator . We show that the deviation , adequately normalized, is asymptotically normal and specify the asymptotic variance.

Complex systems are composed of mutually interacting components and the output values of these components usually exhibit long-range cross-correlations. Using wavelet analysis, we propose a method of characterizing the joint multifractal nature of these long-range cross correlations, a method we call multifractal cross wavelet analysis (MFXWT). We assess the performance of the MFXWT method by performing extensive numerical experiments on the dual binomial measures with multifractal cross correlations and the bivariate fractional Brownian motions (bFBMs) with monofractal cross correlations. For binomial multifractal measures, we find the empirical joint multifractality of MFXWT to be in approximate agreement with the theoretical formula. For bFBMs, MFXWT may provide spurious multifractality because of the wide spanning range of the multifractal spectrum. We also apply the MFXWT method to stock market indices, and in pairs of index returns and volatilities we find an intriguing joint multifractal behavior. The tests on surrogate series also reveal that the cross correlation behavior, particularly the cross correlation with zero lag, is the main origin of cross multifractality.

Prediction of river discharge is important for water resources management. Engineers have developed many physical and mathematical models for prediction of river discharge. The fact that physical hydrological models are site specific and include many parameters, has led researchers to work on mathematical black-box models. In this study, the fuzzy time series (FTS) method was used in the prediction of river discharge. The proposed method, which is employed for the first time in hydrology, allows to fast decision-making mechanism. The proposed algorithm, FTS, is used along with continuous wavelet transform (CWT) method to improve prediction performance. CWT, can be used as pre-treatment technique, is able decompose concerned time series into several bands at different scales which allows to predict much more homogeneous series rather than complex flow discharge series. By considering various statistical success criteria, the wavelet transformed time series (WFTS) method performed quite high accurate predictions compared to the classical fuzzy time series method. Combining FTS with wavelet transform opens a new window in the fuzzy time series method applications that has ability to improve the prediction performance.

S&P 500 index data sampled at one-minute intervals over the course of 11.5 years (January 1989–May 2000) is analyzed, and in particular the Hurst parameter over segments of stationarity (the time period over which the Hurst parameter is almost constant) is estimated. An asymptotically unbiased and efficient estimator using the log-scale spectrum is employed. The estimator is asymptotically Gaussian and the variance of the estimate that is obtained from a data segment of N points is of order . Wavelet analysis is tailor-made for the high frequency data set, since it has low computational complexity due to the pyramidal algorithm for computing the detail coefficients. This estimator is robust to additive non-stationarities, and here it is shown to exhibit some degree of robustness to multiplicative non-stationarities, such as seasonalities and volatility persistence, as well. This analysis suggests that the market became more efficient in the period 1997–2000.