How much popular is table tennis? Googling for table tennis (Preprint)

2019 ◽  
Author(s):  
Nicola Bragazzi
Keyword(s):  
Real Time ◽  
Moving Average ◽  
Team Sports ◽  
Arma Model ◽  
Autoregressive Moving Average ◽  
Table Tennis ◽  
Cyclic Pattern ◽  
Web Searches ◽  
The World ◽  
Internet Searches

BACKGROUND Table tennis is a popular sport, practiced worldwide. In particular, it is the most popular racquet sport in the world and ranks second in terms of participation. Over 10-18 million players compete in many tournaments each year. OBJECTIVE However, little is known about the table tennis related web activities. METHODS Google Trends (GT), a freely online available tool that tracks and monitor web searches, was mined from 2004 to today worldwide, using table tennis as a keyword. The searches volumes were correlated with country ranking (overall and broken down for gender). An autoregressive moving-average (ARMA) model was used to model the GT-generated data. RESULTS Table tennis resulted the second most popular racquet sport being googled worldwide. Interest for table tennis has slightly decreased over the time with table tennis-related web activities exhibiting a cyclic pattern. The best ARMA model which fits the trend of Internet searches is an ARMA(6,5). The most related searches concerned equipment and sports products for playing table tennis, famous athletes, other team sports and scheduled tournaments. Country ranking (both overall and broken down for gender) well correlated with GT-based RSV. CONCLUSIONS Despite its limitations, GT appears to have some degrees of utility in tracking, monitoring in real time and forecasting/nowcasting table tennis related web searches.

scholarly journals GA-ARMA Model for Predicting IGS RTS Corrections

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Mingyu Kim ◽  
Jeongrae Kim
Keyword(s):  
Real Time ◽  
Moving Average ◽  
Arma Model ◽  
Satellite Orbit ◽  
Satellite System ◽  
Prediction Performance ◽  
Data Loss ◽  
International Gnss Service ◽  
Time Service ◽  
Hardware Failures

The global navigation satellite system (GNSS) is widely used to estimate user positions. For precise positioning, users should correct for GNSS error components such as satellite orbit and clock errors as well as ionospheric delay. The international GNSS service (IGS) real-time service (RTS) can be used to correct orbit and clock errors in real-time. Since the IGS RTS provides real-time corrections via the Internet, intermittent data loss can occur due to software or hardware failures. We propose applying a genetic algorithm autoregressive moving average (GA-ARMA) model to predict the IGS RTS corrections during data loss periods. The RTS orbit and clock corrections are predicted up to 900 s via the GA-ARMA model, and the prediction accuracies are compared with the results from a generic ARMA model. The orbit prediction performance of the GA-ARMA is nearly equivalent to that of ARMA, but GA-ARMA’s clock prediction performance is clearly better than that of ARMA, achieving a 32% error reduction. Predicted RTS corrections are applied to the broadcast ephemeris, and precise point positioning accuracies are compared. GA-ARMA shows a significant accuracy improvement over ARMA, particularly in terms of vertical positioning.

Application of Kalman Filtering for Natural Gray Image Denoising

2011 ◽  
Vol 187 ◽  
pp. 92-96 ◽  
Author(s):  
Zhi Kai Huang ◽  
De Hui Liu ◽  
Xing Wang Zhang ◽  
Ling Ying Hou
Keyword(s):  
Kalman Filter ◽  
Image Denoising ◽  
Kalman Filtering ◽  
Moving Average ◽  
Arma Model ◽  
Adaptive Method ◽  
Natural Image ◽  
Autoregressive Moving Average ◽  
Processing Step ◽  
Reduction Methods

Image denoising is one of the classical problems in digital image processing, and has been studied for nearly half a century due to its important role as a pre-processing step in various image applications. In this work, a denoising algorithm based on Kalman filtering was used to improve natural image quality. We have studied noise reduction methods using a hybrid Kalman filter with an autoregressive moving average (ARMA) model that the coefficients of the AR models for the Kalman filter are calculated by solving for the minimum square error solutions of over-determined linear systems. Experimental results show that as an adaptive method, the algorithm reduces the noise while retaining the image details much better than conventional algorithms.

scholarly journals Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes

2005 ◽  
Vol 12 (1) ◽  
pp. 55-66 ◽  
Author(s):  
W. Wang ◽  
P. H. A. J. M Van Gelder ◽  
J. K. Vrijling ◽  
J. Ma
Keyword(s):  
Seasonal Variation ◽  
Moving Average ◽  
Arma Model ◽  
Autoregressive Moving Average ◽  
Conditional Heteroskedasticity ◽  
Daily Streamflow ◽  
Arch Effect ◽  
Monthly Streamflow ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Residual Series

Abstract. Conventional streamflow models operate under the assumption of constant variance or season-dependent variances (e.g. ARMA (AutoRegressive Moving Average) models for deseasonalized streamflow series and PARMA (Periodic AutoRegressive Moving Average) models for seasonal streamflow series). However, with McLeod-Li test and Engle's Lagrange Multiplier test, clear evidences are found for the existence of autoregressive conditional heteroskedasticity (i.e. the ARCH (AutoRegressive Conditional Heteroskedasticity) effect), a nonlinear phenomenon of the variance behaviour, in the residual series from linear models fitted to daily and monthly streamflow processes of the upper Yellow River, China. It is shown that the major cause of the ARCH effect is the seasonal variation in variance of the residual series. However, while the seasonal variation in variance can fully explain the ARCH effect for monthly streamflow, it is only a partial explanation for daily flow. It is also shown that while the periodic autoregressive moving average model is adequate in modelling monthly flows, no model is adequate in modelling daily streamflow processes because none of the conventional time series models takes the seasonal variation in variance, as well as the ARCH effect in the residuals, into account. Therefore, an ARMA-GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) error model is proposed to capture the ARCH effect present in daily streamflow series, as well as to preserve seasonal variation in variance in the residuals. The ARMA-GARCH error model combines an ARMA model for modelling the mean behaviour and a GARCH model for modelling the variance behaviour of the residuals from the ARMA model. Since the GARCH model is not followed widely in statistical hydrology, the work can be a useful addition in terms of statistical modelling of daily streamflow processes for the hydrological community.

scholarly journals A Fuzzy Set-Valued Autoregressive Moving Average Model and Its Applications

Symmetry ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 324 ◽  
Author(s):  
Dabuxilatu Wang ◽  
Liang Zhang
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Empirical Analysis ◽  
Moving Average ◽  
Arma Model ◽  
Autoregressive Moving Average ◽  
Complex Data ◽  
Arma Models ◽  
Linguistic Data ◽  
Series Analysis

Autoregressive moving average (ARMA) models are important in many fields and applications, although they are most widely applied in time series analysis. Expanding the ARMA models to the case of various complex data is arguably one of the more challenging problems in time series analysis and mathematical statistics. In this study, we extended the ARMA model to the case of linguistic data that can be modeled by some symmetric fuzzy sets, and where the relations between the linguistic data of the time series can be considered as the ordinary stochastic correlation rather than fuzzy logical relations. Therefore, the concepts of set-valued or interval-valued random variables can be employed, and the notions of Aumann expectation, Fréchet variance, and covariance, as well as standardized process, were used to construct the ARMA model. We firstly determined that the estimators from the least square estimation of the ARMA (1,1) model under some L2 distance between two sets are weakly consistent. Moreover, the justified linguistic data-valued ARMA model was applied to forecast the linguistic monthly Hang Seng Index (HSI) as an empirical analysis. The obtained results from the empirical analysis indicate that the accuracy of the prediction produced from the proposed model is better than that produced from the classical one-order, two-order, three-order autoregressive (AR(1), AR(2), AR(3)) models, as well as the (1,1)-order autoregressive moving average (ARMA(1,1)) model.

A Novel Adaptive Integrated Navigation Filtering Method Based on ARMA/GARCH Model

2013 ◽  
Vol 462-463 ◽  
pp. 259-266
Author(s):  
Xin Zhao ◽  
Hong Lei Qin ◽  
Li Cong
Keyword(s):  
Moving Average ◽  
Garch Model ◽  
Main Idea ◽  
Arma Model ◽  
Statistical Characteristics ◽  
Autoregressive Moving Average ◽  
Integrated Navigation ◽  
Conditional Mean ◽  
Filtering Method ◽  
Residual Series

This paper proposes a novel adaptive integrated navigation filtering method based on autoregressive moving average (ARMA) model and generalized autoregressive conditional heteroscedasticity (GARCH) model. The main idea in this study is to employ ARMA/GARCH model to estimate statistical characteristics of filtering residual series online, namely, the conditional mean and conditional standard deviation, and then the filter parameters are adaptively adjusted based on forecasted results of ARMA/GARCH model in order to improve the reliability of the system when there are abnormal disturbance and other uncertain factors in real condition. On this basis, experiment is used to verify the validity of the method. The simulation results demonstrate that the ARMA/GARCH model can well capture the unusual condition of GPS receiver output, and this adaptive filtering method can effectively improve the reliability of the system.

Parameter Identification of Time Varying System Using Basic Function Expansion

2011 ◽  
Vol 403-408 ◽  
pp. 2800-2804
Author(s):  
En Wei Chen ◽  
Yi Min Lu ◽  
Zheng Shi Liu ◽  
Yong Wang
Keyword(s):  
High Efficiency ◽  
Moving Average ◽  
Arma Model ◽  
Basic Function ◽  
Least Square ◽  
Autoregressive Moving Average ◽  
Time Varying ◽  
Varying Parameters ◽  
Time Varying Parameters ◽  
Time Invariant

Time-varying parameters identification in linear system is considered, which can be changed into time-invariant coefficient polynomials after Taylor expansion. Using response data to establish the time-varying autoregressive moving average (TV-ARMA) model, then utilizing least-square algorithm to obtain time-invariant coefficients of time-varying parameters. According to error analysis, to reduce errors and improve accuracy, the estimation time is divided into small internals and the above method is used in each interval. Simulation shows that, under certain error condition, the time-varying parameters obtained by the method have good agreement with the theoretical values; the measures taken have strong anti-interference and high efficiency.

scholarly journals Minimum Message Length in Hybrid ARMA and LSTM Model Forecasting

2021 ◽  
Author(s):  
Zheng Fang ◽  
David L. Dowe ◽  
Shelton Peiris ◽  
Dedi Rosadi
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Short Term Memory ◽  
Moving Average ◽  
Arma Model ◽  
Autoregressive Moving Average ◽  
Information Theoretic ◽  
Minimum Message Length ◽  
Message Length ◽  
Long Short Term Memory

We investigate the power of time series analysis based on a variety of information-theoretic approaches from statistics (AIC, BIC) and machine learning (Minimum Message Length) - and we then compare their efficacy with traditional time series model and with hybrids involving deep learning. More specifically, we develop AIC, BIC and Minimum Message Length (MML) ARMA (autoregressive moving average) time series models - with this Bayesian information-theoretic MML ARMA modelling already being new work. We then study deep learning based algorithms in time series forecasting, using Long Short Term Memory (LSTM), and we then combine this with the ARMA modelling to produce a hybrid ARMA-LSTM prediction. Part of the purpose of the use of LSTM is to seek capture any hidden information in the residuals left from the traditional ARMA model. We show that MML not only outperforms earlier statistical approaches to ARMA modelling, but we further show that the hybrid MML ARMA-LSTM models outperform both ARMA models and LSTM models.

scholarly journals Groundwater Depth Forecasting Using a Coupled Model

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Manfei Zhang ◽  
Yimeng Wang ◽  
Xiao Wang ◽  
Weibo Zhou
Keyword(s):  
Moving Average ◽  
Coupled Model ◽  
Arma Model ◽  
Groundwater Depth ◽  
Irrigation District ◽  
Autoregressive Moving Average ◽  
Annual Average ◽  
Canal Irrigation ◽  
Leave One Out ◽  
Bp Model

Accurate and reliable prediction of groundwater depth is a critical component in water resources management. In this paper, a new method based on coupling wavelet decomposition method (WA), autoregressive moving average (ARMA) model, and BP neural network (BP) model for groundwater depth forecasting applications was proposed. The relative performance of the proposed coupled model (WA-ARMA-BP) was compared to the regular autoregressive integrated moving average (ARIMA) and BP models for annual average groundwater depth forecasting using leave-one-out cross-validation (LOO-CV). The variables used to develop and validate the models were average groundwater depth data recorded from 1981 to 2010 in Jinghui Canal Irrigation District in the northwest of China. It was found that the WA-ARMA-BP model provided more accurate annual average groundwater depth forecasts compared to the ARIMA and BP models. The results of the study indicate the potential of the WA-ARMA-BP model in forecasting nonstationary time series such as groundwater depth.

The limitations and usefulness of streamflow generation methods: a case study

1980 ◽  
Vol 7 (1) ◽  
pp. 185-191
Author(s):  
W. J. Stolte
Keyword(s):  
Probabilistic Models ◽  
Moving Average ◽  
Arma Model ◽  
Physical World ◽  
Model Complexity ◽  
Autoregressive Moving Average ◽  
Streamflow Generation ◽  
Recorded Data ◽  
Hydrologic Design

Probabilistic models have become important hydrologic tools. However, increasing model complexity makes the connections between the model and the physical world more and more vague. This can lead to a de-emphasis of engineering judgment, since model validity is easily assumed when even partial verification must await future occurrences. A simple autoregressive model was used to generate stochastic flow sequences for the dam and reservoir being constructed on the Red Deer River in Alberta. The results from this model were compared with those obtained from a more complex autoregressive moving average (ARMA) model. Both models have similar deficiencies. It is concluded that since stochastic generation can never represent future conditions with certainty, the common practice of basing the hydrologic design of reservoirs on actually recorded data is usually the most valid procedure. However, stochastic streamflow generation can be used to give valuable probabilities of reservoir storage failure.

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