scholarly journals Research on the Influence of Volatility of International Energy Commodity Futures Market on CPI in China

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Keyao Lin ◽  
Chao Xun ◽  
Fei Wang ◽  
Angela Chi Chao ◽  
Zhenyu Du
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
High Frequency ◽  
Prediction Accuracy ◽  
Low Frequency ◽  
Futures Market ◽  
Chinese Economy ◽  
Commodity Futures ◽  
Explanatory Variables ◽  
Daily Data

This article analyses the transmission path of the international commodity futures market’s impact on the Chinese economy. We use the MIDAS model and daily data to predict China’s CPI in real time. Empirical analysis results show that (1) the influence of high-frequency explanatory variables on low-frequency CPI is different. The optimal lag orders of domestic high-frequency variables are all around 23, which can be regarded as one month in practice, indicating that their CPI influence takes one month to show. (2) Both the univariate MIDAS model and the multivariate MIDAS combined prediction model have good performance in prediction accuracy. (3) The predicted results of the multivariate MIDAS combined prediction model for CPI in China’s normal months are relatively excellent. However, when exceptional circumstances occur, the prediction results will show a specific deviation, and the prediction accuracy will also be reduced. Finally, some feasible suggestions are put forward according to the research results.

scholarly journals Offline Algorithms in Low-Frequency Trading

Queue ◽  
2020 ◽  
Vol 18 (6) ◽  
pp. 37-51
Author(s):  
Terence Kelly
Keyword(s):  
Real Time ◽  
Real World ◽  
High Frequency ◽  
Low Frequency ◽  
Combinatorial Auctions ◽  
Hard Problem ◽  
Np Hard ◽  
High Stakes ◽  
Drill Bits ◽  
Np Hard Problem

Expectations run high for software that makes real-world decisions, particularly when money hangs in the balance. This third episode of the Drill Bits column shows how well-designed software can effectively create wealth by optimizing gains from trade in combinatorial auctions. We'll unveil a deep connection between auctions and a classic textbook problem, we'll see that clearing an auction resembles a high-stakes mutant Tetris, we'll learn to stop worrying and love an NP-hard problem that's far from intractable in practice, and we'll contrast the deliberative business of combinatorial auctions with the near-real-time hustle of high-frequency trading. The example software that accompanies this installment of Drill Bits implements two algorithms that clear combinatorial auctions.

Research on Combined Prediction Model for Busy Telephone Traffic

2014 ◽  
Vol 610 ◽  
pp. 789-796
Author(s):  
Jiang Bao Li ◽  
Zhen Hong Jia ◽  
Xi Zhong Qin ◽  
Lei Sheng ◽  
Li Chen
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Low Frequency ◽  
Prediction Method ◽  
Pso Algorithm ◽  
Frequency Component ◽  
Least Square ◽  
High Frequency Component ◽  
Support Vector ◽  
Telephone Traffic

In order to improve the prediction accuracy of busy telephone traffic, this study proposes a busy telephone traffic prediction method that combines wavelet transformation and least square support vector machine (lssvm) model which is optimized by particle swarm optimization (pso) algorithm. Firstly, decompose the pretreatment of busy telephone traffic data with mallat algorithm and get low frequency component and high frequency component. Secondly, reconfigure each component and use pso_lssvm model predict each reconfigured one. Then the busy telephone traffic can be achieved. The experimental results show that the prediction model has higher prediction accuracy and stability.

scholarly journals Fusion of GNSS and Speedometer Based on VMD and Its Application in Bridge Deformation Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 694 ◽  
Author(s):  
Ruicheng Zhang ◽  
Chengfa Gao ◽  
Shuguo Pan ◽  
Rui Shang
Keyword(s):  
Real Time ◽  
High Precision ◽  
High Frequency ◽  
Spectral Response ◽  
Low Frequency ◽  
Deformation Monitoring ◽  
Full Time ◽  
Dynamic Displacement ◽  
Mode Decomposition ◽  
Acceleration Sensors

Real-time dynamic displacement and spectral response on the midspan of Jiangyin Bridge were calculated using Global Navigation Satellite System (GNSS) and a speedometer for the purpose of understanding the dynamic behavior and the temporal evolution of the bridge structure. Considering that the GNSS measurement noise is large and the velocity/acceleration sensors cannot measure the low-frequency displacement, the Variational Mode Decomposition (VMD) algorithm was used to extract the low-frequency displacement of GNSS. Then, the low-frequency displacement extracted from the GNSS time series and the high-frequency vibration calculated by speedometer were combined in this paper in order to obtain the high precision three-dimensional dynamic displacement of the bridge in real time. Simulation experiment and measured data show that the VMD algorithm could effectively resist the modal aliasing caused by noise and discontinuous signals compared with the commonly used Empirical Mode Decomposition (EMD) algorithm, which is guaranteed to get high-precision fusion data. Finally, the fused displacement results can identify high-frequency vibrations and low-frequency displacements of a mm level, which can be used to calculate the spectral characteristics of the bridge and provide reference to evaluate the dynamic and static loads, and the health status of the bridge in the full frequency domain and the full time domain.

scholarly journals Load Prediction Based on Hybrid Model of VMD-mRMR-BPNN-LSSVM

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Gang Zhang ◽  
Hongchi Liu ◽  
Pingli Li ◽  
Meng Li ◽  
Qiang He ◽  
...  
Keyword(s):  
Power System ◽  
High Frequency ◽  
Prediction Accuracy ◽  
Load Forecasting ◽  
Low Frequency ◽  
Load Prediction ◽  
System Load ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Feature Information

Power system load forecasting is an important part of power system scheduling. Since the power system load is easily affected by environmental factors such as weather and time, it has high volatility and multi-frequency. In order to improve the prediction accuracy, this paper proposes a load forecasting method based on variational mode decomposition (VMD) and feature correlation analysis. Firstly, the original load sequence is decomposed using VMD to obtain a series of intrinsic mode function (IMF), it is referred to below as a modal component, and they are divided into high frequency, intermediate frequency, and low frequency signals according to their fluctuation characteristics. Then, the feature information related to the power system load change is collected, and the correlation between each IMF and each feature information is analyzed using the maximum relevance minimum redundancy (mRMR) based on the mutual information to obtain the best feature set of each IMF. Finally, each component is input into the prediction model together with its feature set, in which back propagation neural network (BPNN) is used to predict high-frequency components, least square-support vector machine (LS-SVM) is used to predict intermediate and low frequency components, and BPNN is also used to integrate the prediction results to obtain the final load prediction value, and compare the prediction results of method in this paper with that of the prediction models such as autoregressive moving average model (ARMA), LS-SVM, BPNN, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and VMD. This paper carries out an example analysis based on the data of Xi’an Power Grid Corporation, and the results show that the prediction accuracy of method in this paper is higher.

A nonlinear ultrasonic method for real-time bolt looseness monitoring using PZT transducer–enabled vibro-acoustic modulation

2019 ◽  
Vol 31 (3) ◽  
pp. 364-376 ◽  
Author(s):  
Nan Zhao ◽  
Linsheng Huo ◽  
Gangbing Song
Keyword(s):  
Real Time ◽  
Lead Zirconate Titanate ◽  
High Frequency ◽  
Ultrasonic Method ◽  
Low Frequency ◽  
Lead Zirconate ◽  
Modulation Method ◽  
Nonlinear Ultrasonic ◽  
Acoustic Modulation ◽  
Nonlinear Ultrasonic Method

A real-time nonlinear ultrasonic method based on vibro-acoustic modulation is applied to monitor early bolt looseness quantitatively by using piezoceramic transducers. In addition to the ability to detect the early bolt looseness, a major contribution is that we replaced the shaker, which is commonly used in a vibro-acoustic modulation method, by a permanently installed and low-cost lead zirconate titanate patch. In vibro-acoustic modulation, when stimulating two input waves with distinctive frequencies, namely the high-frequency probing wave and the low-frequency pumping wave, the high-frequency probing wave will be modulated by the low-frequency pumping wave to generate sidebands in terms of bolt looseness. Thus, the influence of low-frequency voltage amplitudes on the modulation results, which is ambiguous in previous research, is also analyzed in this article. The results of experiment demonstrated that the lead zirconate titanate–enabled vibro-acoustic modulation method is reliable and easy to implement to identify the bolt looseness continuously and quantitatively. In addition, low-frequency amplitudes of actuating voltage should be selected in a reasonable range. Finally, we compared the vibro-acoustic modulation method with the time-reversal method based on the linear ultrasonic theory, and the result illustrates that vibro-acoustic modulation method has better performance in monitoring the early bolt looseness.

Development and Evaluation of a Frequency Related Source Intensity Prediction Model of the Traffic Acoustic Noise

2013 ◽  
Vol 807-809 ◽  
pp. 162-167
Author(s):  
Zhen Zhen Ding ◽  
Jian Qiang Zhao ◽  
Ying Chen
Keyword(s):  
Prediction Model ◽  
High Frequency ◽  
Acoustic Noise ◽  
Low Frequency ◽  
Frequency Noise ◽  
Experimental Conditions ◽  
Intensity Prediction ◽  
Start Section ◽  
Spectrum Distribution ◽  
Frequency Area

Two expressway sections including the south section of the beltway in Xian and the start section of the Xi'an-Lantian expressway were selected in this study. By monitoring the acoustic noise level at different frequencies and vehicle speeds, a frequency related source intensity prediction model of the traffic acoustic noise was developed. Experimental results show that the spectrum distribution varies greatly among different kind of vehicles. The noise source intensity produced by large vehicles mainly distribute in low-frequency area. Different from large vehicles, medium vehicles mainly produce medium or high frequency noise, especially when the vehicle's speed is above 60 km/h. When the vehicle's speed is below 60 km/h, the acoustic noise intensity produced by the medium vehicles is relatively weak. The accuracy of the source intensity prediction model is further proved by comparing the predicted data, determined data, and the data obtained in the literature under similar experimental conditions.

scholarly journals Short-Term Traffic Speed Prediction Method for Urban Road Sections Based on Wavelet Transform and Gated Recurrent Unit

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xin Fu ◽  
Wei Luo ◽  
Chengyao Xu ◽  
Xiaoxuan Zhao
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Prediction Model ◽  
High Frequency ◽  
Low Frequency ◽  
Traffic Prediction ◽  
Spatiotemporal Correlation ◽  
Road Section ◽  
Speed Prediction ◽  
Traffic Speed

As a core component of the urban intelligent transportation system, traffic prediction is significant for urban traffic control and guidance. However, it is challenging to achieve accurate traffic prediction due to the complex spatiotemporal correlation of traffic data. A road section speed prediction model based on wavelet transform and neural network is, therefore, proposed in this article to improve traffic prediction methods. The wavelet transform is used to decompose the original traffic speed data, and then the coefficients obtained after the decomposition are used to reconstruct the high-frequency random sequences and the low-frequency trend sequence. Secondly, a GRU neural network is constructed to learn the trend of low-frequency sequence. The spatiotemporal correlation between input data is extracted by adjusting the input of the model. Meanwhile, an ARMA model is used to fit unstable random fluctuations of high-frequency sequences. Last of all, the prediction results of the two models are added together to obtain the final prediction result. The proposed prediction model is validated by using road section speed data based on the floating car data collected in Ningbo. The results show that the proposed model has high accuracy and robustness.

scholarly journals The neural correlates of dreaming

2014 ◽  
Author(s):  
Francesca Siclari ◽  
Benjamin Baird ◽  
Lampros Perogamvros ◽  
Giulio Bernardi ◽  
Joshua J LaRocque ◽  
...  
Keyword(s):  
Real Time ◽  
Rem Sleep ◽  
High Frequency ◽  
Low Frequency ◽  
Nrem Sleep ◽  
Neural Correlates ◽  
High Density ◽  
High Frequency Activity ◽  
Cortical Regions ◽  
Hot Zone

Consciousness never fades during wake. However, if awakened from sleep, sometimes we report dreams and sometimes no experiences. Traditionally, dreaming has been identified with REM sleep, characterized by a wake-like, globally "activated", high-frequency EEG. However, dreaming also occurs in NREM sleep, characterized by prominent low-frequency activity. This challenges our understanding of the neural correlates of conscious experiences in sleep. Using high-density EEG, we contrasted the presence and absence of dreaming within NREM and REM sleep. In both NREM and REM sleep, the presence of dreaming was associated with a local decrease in low-frequency activity in posterior cortical regions. High-frequency activity within these regions correlated with specific dream contents. Monitoring this posterior "hot zone" predicted the presence/absence of dreaming during NREM sleep in real time, suggesting that it may constitute a core correlate of conscious experiences in sleep.

scholarly journals Association between the frequency of surgeries for video-assisted thoracic surgery and the incidence of consequent surgical site infections: a retrospective observational study based on national surveillance data

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Toshiki Kajihara ◽  
Koji Yahara ◽  
Aki Hirabayashi ◽  
Hitomi Kurosu ◽  
Motoyuki Sugai ◽  
...  
Keyword(s):  
High Frequency ◽  
Risk Index ◽  
Univariate Analysis ◽  
Low Frequency ◽  
Surgical Site Infections ◽  
National Surveillance ◽  
Exact Test ◽  
Explanatory Variables ◽  
Video Assisted ◽  
Risk Indices

Abstract Background The association between the frequency of surgeries and the incidence of surgical site infections (SSIs) has been reported for various surgeries. However, no previous study has explored this association among video-assisted thoracic surgeries (VATS). Hence, we aimed to investigate the association between the frequency of surgeries and SSI in video-assisted thoracic surgeries. Methods We analyzed the data of 26,878 thoracic surgeries, including 21,154 VATS, which were collected during a national surveillance in Japan between 2014 and 2018. The frequency of surgeries per hospital department was categorized into low (< 50/year), moderate (50–100/ year), and high (> 100/year). Chi-squared test or Fisher’s exact test was used for discrete explanatory variables, whereas Wilcoxon’s rank-sum test or Kruskal-Wallis test was used for continuous explanatory variables. Univariate analysis of the department groups was conducted to explore confounding factors associated with both SSIs and the department groups. We used a multiple logistic regression model focusing on VATS and stratified by the National Nosocomial Infections Surveillance System (NNIS) risk index. Results The rates of SSIs in the hospital groups with low, moderate, and high frequency of surgeries were 1.39, 1.05, and 1.28%, respectively. In the NNIS risk index 1 stratum, the incidence of SSIs was significantly lower in the moderate-frequency of surgeries group than that in the other groups (odds ratio [OR]: vs. low-frequency of surgeries: 2.48 [95% confidence interval [CI]: 1.20–5.13], P = 0.0143; vs. high-frequency of surgeries: 2.43 [95% CI: 1.44–4.11], P = 0.0009). In the stratum of NNIS risk indices 2 and 3, the incidence of SSI was significantly higher in the low-frequency of surgeries group (OR: 4.83, 95% CI: 1.47–15.93; P = 0.0095). Conclusion The result suggests that for departments with low-frequency of surgeries, an increase in the frequency of surgeries to > 50 per department annually potentially leads to a decrease in the incidence of SSIs. This occurs through an increase in the experience of the departmental surgeons and contributes to the improvement of VATS outcomes in thoracic surgeries.

scholarly journals Reproducibility of Heart Rate Variability Revealed by Repeated Measurements during and after Hemodialysis

2019 ◽  
Vol 49 (3) ◽  
pp. 356-363
Author(s):  
Kerstin Deussing ◽  
Ralph Wendt ◽  
Ronald Burger ◽  
Maik Gollasch ◽  
Joachim Beige
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Real Time ◽  
High Frequency ◽  
Low Frequency ◽  
Repeated Measurements ◽  
Autonomous Nervous System ◽  
Short Term ◽  
Real Time Analysis

Background/Aims: Trajectory of heart rate variability (HRV) represents a noninvasive real-time measure of autonomous nervous system (ANS) and carries the capability of providing new insights into the hemodynamic compensation reserve during hemodialysis (HD). However, studies on HRV reproducibility during HD are scarce and did not refer to different reading periods. In this observational study, we aimed to establish the best suited and most reliable and reproducible HRV index in routine HD treatments including different reading rates. Methods: HRV was characterized by standardized mathematical variation expressions of R/R’ intervals: SD of all R/R’ intervals (ms), square root of the root mean square of the sum of all differences between adjacent R/R’ intervals (ms), percentage of consecutive R/R’ intervals that differ by >50 ms (%), low-frequency spectral analysis HRV (LF, expressing sympathetic activity), and high-frequency HRV (HF, expressing parasympathetic activity). To compare robustness of these HRV indices during HD procedures, we compared HRV indices means between different HD sessions and controlled for association with clinical parameters. Results: In 72 HD treatments of 34 patients, we detected the highest reproducibility (89%) of HRV measures when analyzing the low-frequency to high-frequency (LF/HF) ratio in long-term (3 h) readings. Long-term LF/HF was able to discriminate ­between patients with and without heart failure NYHA classes ≥3 (p = 0.009) and type 2 diabetes (p = 0.023). We were unable to study relationships between ANS and intradialytic complications because they did not appear in our cohort. Short-term readings of HRV indices did not show any significance of pattern change during HD. Conclusion: In summary, our data provide evidence for high robustness of long-term LF/HF in analyzing HRV in HD patients using future automated monitoring systems. For short-term analysis, mathematical real-time analysis must evolve.

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