scholarly journals Wavelet Cross-correlation in Bivariate Time-Series Analysis

2018 ◽  
Vol 19 (3) ◽  
pp. 391
Author(s):  
Eniuce Menezes de Souza ◽  
Vinícius Basseto Félix
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Cross Correlation ◽  
Multiple Scales ◽  
Statistical Significance ◽  
Dependent Data ◽  
Data Sets ◽  
Translation Invariant ◽  
Series Analysis ◽  
Bivariate Time Series

The estimation of the correlation between independent data sets using classical estimators, such as the Pearson coefficient, is well established in the literature. However, such estimators are inadequate for analyzing the correlation among dependent data. There are several types of dependence, the most common being the serial (temporal) and spatial dependence, which are inherent to the data sets from several fields. Using a bivariate time-series analysis, the relation between two series can be assessed. Further, as one time series may be related to an other with a time offset (either to the past or to the future), it is essential to also consider lagged correlations. The cross-correlation function (CCF), which assumes that each series has a normal distribution and is not autocorrelated, is used frequently. However, even when a time series is normally distributed, the autocorrelation is still inherent to one or both time series, compromising the estimates obtained using the CCF and their interpretations. To address this issue, analysis using the wavelet cross-correlation (WCC) has been proposed. WCC is based on the non-decimated wavelet transform (NDWT), which is translation invariant and decomposes dependent data into multiple scales, each representing the behavior of a different frequency band. To demonstrate the applicability of this method, we analyze simulated and real time series from different stochastic processes. The results demonstrated that analyses based on the CCF can be misleading; however, WCC can be used to correctly identify the correlation on each scale. Furthermore, the confidence interval (CI) for the results of the WCC analysis was estimated to determine the statistical significance.

Provincial Level Time Series Analysis : Community Mobility and COVID-19 Daily Cases in Turin, Italy (Preprint)

2021 ◽  
Author(s):  
Dayana Benny
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Cross Correlation ◽  
Provincial Level ◽  
Epidemic Curve ◽  
The Past ◽  
Series Analysis ◽  
Community Mobility ◽  
Cross Correlation Analysis ◽  
Restrictive Measures

BACKGROUND Turin, a province in the Piedmont region sees second highest new COVID-19 infections in Northern part of Italy as of March 31, 2021. During the first wave of pandemic, many restrictive measures were introduced in this province. There are many studies that conducted time series analysis of various regions in Italy, but studies that are analysing the data in province level are limited. Also, no applications of Cross Correlation Function(CCF) have been proposed to analyse relationships between COVID-19 new cases and community mobility at the provincial level in Italy. OBJECTIVE The goal of this time series analysis is to find how the restrictive measures in Turin province, Italy impacted community mobility and helped in flattening the epidemic curve during the first wave of the pandemic. METHODS A simple time series analysis is conducted in this study to analyse whether there is an association between COVID-19 daily cases and community mobility. In this study, we analysed whether the time series of the parameter that estimates the reproduction of infection in the outbreak is related to the past lags of community mobility time series by performing cross-correlation analysis. RESULTS Multiple regression is carried out in which the R0 variable is a linear function of past lags 6, 7, 8, and 1 of the community mobility variable and all coefficients are statistically significant where P = 0.024043, 2.69e-05, 0.045350 and 0.000117 respectively. The cross-correlation between data fitted from the significant past lags of community mobility and transformed basic reproduction number (R0) time-series is obtained in such a manner that the R0 of a day is related to the past lags of community mobility in Turin province. CONCLUSIONS Our analysis shows that the restrictive measures are having an impact on community mobility during the first wave of COVID-19 and it can be related to the reported secondary cases of COVID-19 in Turin province at that time. Through further improvement, this simple model could serve as preliminary research for developing right preventive methods during the early stages of an epidemic.

Provincial Level Time Series Analysis : Community Mobility and COVID-19 Daily Cases in Turin, Italy (Preprint)

2021 ◽  
Author(s):  
Dayana Benny
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Cross Correlation ◽  
Provincial Level ◽  
Epidemic Curve ◽  
The Past ◽  
Series Analysis ◽  
Community Mobility ◽  
Cross Correlation Analysis ◽  
Restrictive Measures

BACKGROUND Turin, a province in the Piedmont region sees second highest new COVID-19 infections in Northern part of Italy as of March 31, 2021. During the first wave of pandemic, many restrictive measures were introduced in this province. There are many studies that conducted time series analysis of various regions in Italy, but studies that are analysing the data in province level are limited. Also, no applications of Cross Correlation Function(CCF) have been proposed to analyse relationships between COVID-19 new cases and community mobility at the provincial level in Italy. OBJECTIVE The goal of this time series analysis is to find how the restrictive measures in Turin province, Italy impacted community mobility and helped in flattening the epidemic curve during the first wave of the pandemic. METHODS A simple time series analysis is conducted in this study to analyse whether there is an association between COVID-19 daily cases and community mobility. In this study, we analysed whether the time series of the parameter that estimates the reproduction of infection in the outbreak is related to the past lags of community mobility time series by performing cross-correlation analysis. RESULTS Multiple regression is carried out in which the R0 variable is a linear function of past lags 6, 7, 8, and 1 of the community mobility variable and all coefficients are statistically significant where P = 0.024043, 2.69e-05, 0.045350 and 0.000117 respectively. The cross-correlation between data fitted from the significant past lags of community mobility and transformed basic reproduction number (R0) time-series is obtained in such a manner that the R0 of a day is related to the past lags of community mobility in Turin province. CONCLUSIONS Our analysis shows that the restrictive measures are having an impact on community mobility during the first wave of COVID-19 and it can be related to the reported secondary cases of COVID-19 in Turin province at that time. Through further improvement, this simple model could serve as preliminary research for developing right preventive methods during the early stages of an epidemic.

Time series analysis of long-term data sets of atmospheric mercury concentrations

2004 ◽  
Vol 380 (3) ◽  
pp. 493-501 ◽  
Author(s):  
Christian Temme ◽  
Ralf Ebinghaus ◽  
J�rgen W. Einax ◽  
Alexandra Steffen ◽  
William H. Schroeder
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Atmospheric Mercury ◽  
Data Sets ◽  
Series Analysis ◽  
Term Data

Intraindividual reference values

2004 ◽  
Vol 42 (7) ◽  
Author(s):  
Josep M. Queraltó
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Statistical Significance ◽  
Reference Intervals ◽  
First Order ◽  
Series Analysis ◽  
Statistical Framework ◽  
Reliable Model ◽  
Key Concepts ◽  
High Degree

AbstractWhen a biological quantity examination exhibits a high degree of individuality, developing a strategy for interpreting these values in an individual context can be a useful alternative. Time-series analysis is the appropriate statistical framework to build a model for explanation of the behaviour of laboratory information and to forecast future values. The key concepts in this approach are autocorrelation and withinperson variance. Unfortunately, the powerful tools provided by time-series analysis require many observations, a requisite difficult to meet in every day practice. However, introducing some restrictions in the autocorrelation parameter of the most reliable model, the first order autocorrelation model, and using the average within-person variance from a selected population, it is possible to build predictive reference intervals for an individual, based on only few observations. The most common case is the minimum time series: when there are just two observations. The statistical significance of the change from a previous observation is a problem that arises from both quality control (delta checks) and the interpretative diagnostic fields (reference change limit). Applying the same restrictive criteria, it is possible to develop specific limits for a difference between consecutive observations based on a within-person variance selected from the distribution of variances found in a sample of similar individuals.

scholarly journals Study on Time Series Analysis of Some Hydrological Quantities. (2). The Proposal of the Bivariate Time Series Analysis.

2002 ◽  
Vol 15 (1) ◽  
pp. 23-38
Author(s):  
Mitsuya ENOKIDA ◽  
Mututoshi FUKUDA ◽  
Hiroshi SHIMIZU ◽  
Risaku FUKUI ◽  
Hitoshi ICHIKAWA ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Series Analysis ◽  
Bivariate Time Series

scholarly journals APLIKASI TIME SERIES ANALYSIS UNTUK KARAKTERISASI AKUIFER KARST

2018 ◽  
Author(s):  
Afid Nurkholis ◽  
Tjahyo Nugroho Adji ◽  
Ahmad Cahyadi
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Spectral Density ◽  
Phase Function ◽  
Cross Correlation ◽  
Black Box ◽  
Gain Function ◽  
Coherency Function ◽  
Series Analysis

Time series analysis merupakan suatu analisis statistik yang mencerminkan respons sistem karst terhadap curah hujan. Konsep dari metode ini adalah menganggap sistem akuifer karst sebagai black box yang tidak diketahui kinerjanya. Time series analysis menggunakan perhitungan statistik berupa univariate (autocorrelation) dan bivariate (cross-correlation). Kedua metode menganalisis data berdasarkan waktu dan dapat diubah bentuk menjadi analisis frekuensi. Autocorrellation dapat diubah menjadi spectral density. Cross-correlation dapat diubah menjadi cross-amplitude, phase function, coherency function, dan gain function. Tulisan ini akan menjelaskan langkah-langkah perhitungan seluruh metode time series analysis tersebut untuk melakukan karakterisasi akuifer karst. Data yang digunakanadalah pasangan debit aliran dan curah hujan selama 6 bulan (1 Januari 2017 – 30 Juni 2017). Kedua data dicatat pada Gua Pindul (sebagai outlet sistem akuifer karst)dan Sinking Stream Kedungbuntung. Hasil perhitungan menunjukkan bahwa time series analysis dapat diklasifikan menjadi pelepasan aliran conduit, fissure, dandiffuse.

scholarly journals Performance Consistency of International Soccer Teams in Euro 2012: a Time Series Analysis

2013 ◽  
Vol 38 ◽  
pp. 213-226 ◽  
Author(s):  
Mohsen Shafizadeh ◽  
Marc Taylor ◽  
Carlos Lago Peñas
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Performance Indicators ◽  
Final Stage ◽  
Cross Correlation ◽  
Positive Correlation ◽  
Series Analysis ◽  
Cross Correlation Analysis ◽  
Soccer Teams ◽  
Consistent Performance

Abstract The purpose of this study was to examine the consistency of performance in successive matches for international soccer teams from Europe which qualified for the quarter final stage of EURO 2012 in Poland and Ukraine. The eight teams that reached the quarter final stage and beyond were the sample teams for this time series analysis. The autocorrelation and cross-correlation functions were used to analyze the consistency of play and its association with the result of match in sixteen performance indicators of each team. The results of autocorrelation function showed that based on the number of consistent performance indicators, Spain and Italy demonstrated more consistency in successive matches in relation to other teams. This appears intuitive given that Spain played Italy in the final. However, it is arguable that other teams played at a higher performance levels at various parts of the competition, as opposed to performing consistently throughout the tournament. The results of the cross-correlation analysis showed that in relation to goal-related indicators, these had higher associations with the match results of Spain and France. In relation to the offensive-related indicators, France, England, Portugal, Greece, Czech Republic and Spain showed a positive correlation with the match result. In relation to the defensive-related indicators, France, England, Greece and Portugal showed a positive correlation with match results. In conclusion, in an international soccer tournament, the successful teams displayed a greater degree of performance consistency across all indicators in comparison to their competitors who occasionally would show higher levels of performance in individual games, yet not consistently across the overall tournament. The authors therefore conclude that performance consistency is more significant in international tournament soccer, versus occasionally excelling in some metrics and indicators in particular games.

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