Measurement of the parameters of slowly time varying high frequency transients

1989 ◽  
Vol 38 (6) ◽  
pp. 1057-1063 ◽  
Author(s):  
A.A. Girgis ◽  
J. Qiu
Keyword(s):  
High Frequency ◽  
Time Varying

scholarly journals Frequency variations of gravity waves interacting with a time-varying tide

2013 ◽  
Vol 31 (10) ◽  
pp. 1731-1743 ◽  
Author(s):  
C. M. Huang ◽  
S. D. Zhang ◽  
F. Yi ◽  
K. M. Huang ◽  
Y. H. Zhang ◽  
...  
Keyword(s):  
Gravity Waves ◽  
High Frequency ◽  
Lower Atmosphere ◽  
Horizontal Wind ◽  
Frequency Variation ◽  
Time Varying ◽  
Transient Nature ◽  
Critical Layers ◽  
Favorable Conditions ◽  
Frequency Variations

Abstract. Using a nonlinear, 2-D time-dependent numerical model, we simulate the propagation of gravity waves (GWs) in a time-varying tide. Our simulations show that when a GW packet propagates in a time-varying tidal-wind environment, not only its intrinsic frequency but also its ground-based frequency would change significantly. The tidal horizontal-wind acceleration dominates the GW frequency variation. Positive (negative) accelerations induce frequency increases (decreases) with time. More interestingly, tidal-wind acceleration near the critical layers always causes the GW frequency to increase, which may partially explain the observations that high-frequency GW components are more dominant in the middle and upper atmosphere than in the lower atmosphere. The combination of the increased ground-based frequency of propagating GWs in a time-varying tidal-wind field and the transient nature of the critical layer induced by a time-varying tidal zonal wind creates favorable conditions for GWs to penetrate their originally expected critical layers. Consequently, GWs have an impact on the background atmosphere at much higher altitudes than expected, which indicates that the dynamical effects of tidal–GW interactions are more complicated than usually taken into account by GW parameterizations in global models.

scholarly journals Estimating dynamic copula dependence using intraday data

2015 ◽  
Vol 19 (4) ◽  
Author(s):  
Lidan Grossmass ◽  
Ser-Huang Poon
Keyword(s):  
Stock Returns ◽  
Long Memory ◽  
High Frequency ◽  
Time Varying ◽  
Credit Crisis ◽  
Dynamic Copula ◽  
Tick Data ◽  
Out Of Sample ◽  
Intraday Data ◽  
Better Than

AbstractWe estimate the dynamic daily dependence between assets by applying the Semiparametric Copula-Based Multivariate Dynamic (SCOMDY) model on intraday data. Using tick data of three stock returns of the period before and during the credit crisis, we find that our dependence estimator better captures the steep increase in dependence during the onset of the crisis as compared to other commonly used time-varying copula methods. Like other high-frequency estimators, we find that the dependence estimator exhibits long memory and forecast it using a HAR model. We show that for out-of-sample forecasts, our dependence estimator performs better than the constant estimator and other commonly used time-varying copula dependence estimators.

Robust Tracking Time-Varying High-Frequency CW Telegraph Signal

2012 ◽  
Vol 155-156 ◽  
pp. 435-439
Author(s):  
Guo Jun Li ◽  
Xiao Na Zhou ◽  
Nai Qian Liu ◽  
Shao Hua Li
Keyword(s):  
High Frequency ◽  
Continuous Wave ◽  
Impulsive Noise ◽  
Model Simulation ◽  
Time Varying ◽  
Simulation Studies ◽  
Robust Tracking ◽  
Robust Kalman Filter ◽  
Tracking Time ◽  
Nonlinear Statistical Model

Continuous wave (CW) telegraph is a crucial communication means for high-frequency tactical communication. But there is serious frequency deviation and impulsive noise in High-frequency channel, thus the conventional tracking method based on Gaussian noise assumption may lose the track of time-varying CW signal. A new robust kalman filter-based tracker is proposed in this paper to extract the time-varying CW signal in presence of impulsive interference, which uses a nonlinear statistical model. Simulation studies show this method can dynamically track nonstationary CW signal and effectively suppress burst impulse noise.

scholarly journals Multiple STL decomposition in discovering a multi-seasonality of intraday trading volume

2021 ◽  
Vol 12 (1) ◽  
pp. 61-74
Author(s):  
Josip Arneric
Keyword(s):  
High Frequency ◽  
Trading Volume ◽  
Missing Values ◽  
High Frequency Data ◽  
Seasonal Patterns ◽  
Time Varying ◽  
Frequency Data ◽  
Seasonal Component ◽  
Univariate Time Series ◽  
Over Time

The seasonal and trend decomposition of a univariate time-series based on Loess (STL) has several advantages over traditional methods. It deals with any periodicity length, enables seasonality change over time, allows missing values, and is robust to outliers. However, it does not handle trading day variation by default. This study offers how to deal with this drawback. By applying multiple STL decompositions of 15-minute trading volume observations, three seasonal patterns were discovered: hourly, daily, and monthly. The research objective was not only to discover if multi-seasonality exists in trading volume by employing high-frequency data but also to determine which seasonal component is most time-varying, and which seasonal components are the strongest or weakest when comparing the variation in the magnitude between them. The results indicate that hourly seasonality is the strongest, while daily seasonality changes the most. A better understanding of trading volume multiple patterns can be very helpful in improving the performance of trading algorithms.

Cardiovascular variability after arousal from sleep: time-varying spectral analysis

2003 ◽  
Vol 95 (4) ◽  
pp. 1394-1404 ◽  
Author(s):  
Anna Blasi ◽  
Javier Jo ◽  
Edwin Valladares ◽  
Barbara J. Morgan ◽  
James B. Skatrud ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
High Frequency ◽  
Low Frequency ◽  
Sleep Time ◽  
Respiratory Pattern ◽  
Time Varying ◽  
High Frequency Power ◽  
Low Frequency Power ◽  
Frequency Power

We performed time-varying spectral analyses of heart rate variability (HRV) and blood pressure variability (BPV) recorded from 16 normal humans during acoustically induced arousals from sleep. Time-varying autoregressive modeling was employed to estimate the time courses of high-frequency HRV power, low-frequency HRV power, the ratio between low-frequency and high-frequency HRV power, and low-frequency power of systolic BPV. To delineate the influence of respiration on HRV, we also computed respiratory airflow high-frequency power, the modified ratio of low-frequency to high-frequency HRV power, and the average transfer gain between respiration and heart rate. During cortical arousal, muscle sympathetic nerve activity and heart rate increased and returned rapidly to baseline, but systolic blood pressure, the ratio between low-frequency and high-frequency HRV power, low-frequency HRV power, the modified ratio of low-frequency to high-frequency HRV power, and low-frequency power of systolic BPV displayed increases that remained above baseline up to 40 s after arousal. High-frequency HRV power and airflow high-frequency power showed concommitant decreases to levels below baseline, whereas the average transfer gain between respiration and heart rate remained unchanged. These findings suggest that 1) arousal-induced changes in parasympathetic activity are strongly coupled to respiratory pattern and 2) the sympathoexcitatory cardiovascular effects of arousal are relatively long lasting and may accumulate if repetitive arousals occur in close succession.

scholarly journals Intraday and intraweek trade anomalies on the Czech stock market

2012 ◽  
Vol 60 (4) ◽  
pp. 79-88 ◽  
Author(s):  
Oleg Deev ◽  
Dagmar Linnertová
Keyword(s):  
Stock Market ◽  
High Frequency ◽  
Regression Models ◽  
Global Financial Crisis ◽  
Equity Market ◽  
High Frequency Data ◽  
Time Varying ◽  
Market Returns ◽  
The Global Financial Crisis ◽  
The Eu

The paper examines intraday and intraweek market returns on the Czech stock market for the search of time and seasonal anomalies in its activities during the last ten years. Existence or absence of anomalies indicates the efficiency of the market. A group of regression models and GARCH (1,1) model is used for the analysis of daily and high frequency data of the PX index. Time varying nature of market seasonalities is revealed with the Czech equity market having implications for changing efficiency over the studied period, when the Czech Republic’s accession to the EU implied the increase in efficiency and the global financial crisis led to opposite results and regularities, which are not yet fully overcomed. Additionally, significant hour-of-the-day effect (open jump effect) in the index returns is established.

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