Headache and Mood: A Time-Series Analysis of Self-Ratings

Cephalalgia ◽  
1984 ◽  
Vol 4 (1) ◽  
pp. 45-52 ◽  
Author(s):  
Jan Dalkvist ◽  
Karl Ekbom ◽  
Elisabet Waldenlind

Self-ratings with respect to headache and five mood dimensions were obtained twice daily from five patients suffering from migraine and six patients suffering from muscle-contraction headache during a mean period of 47.9 days (range: 38–61). The data were analysed by multiple regression, with the rated headache as dependent variable. Different time intervals between measurement of the independent variables and measurement of the dependent variable were used. A significant time-dependent relation was found between the migraine ratings and the alertness ratings. Significant time-dependent relations were also found between rated muscle-contraction headache and rated anger and alertness, respectively, but the trends were not very pronounced. In the case of no time lag, rated muscle-contraction headache tended to be negatively related to rated alertness, happiness and concentration. Significant periodic trends were found for both the migraine and the muscle-contraction headache. The major findings are discussed in terms of stress and biological rhythms.

2021 ◽  
pp. 2250012
Author(s):  
G. F. Zebende ◽  
E. F. Guedes

A correlogram is a statistical tool that is used to check time-series memory by computing the auto-correlation coefficient as a function of the time lag. If the time-series has no memory, then the auto-correlation must be close to zero for any time lag, otherwise if there is a memory, then the auto-correlations must be significantly different from zero. Therefore, based on the robust detrended cross-correlation coefficient, [Formula: see text], we propose the detrended correlogram method in this paper, which will be tested for some time-series (simulated and empirical). This new statistical tool is able to visualize a complete map of the auto-correlation for many time lags and time-scales, and can therefore analyze the memory effect for any time-series.


2008 ◽  
Vol 387 (13) ◽  
pp. 3145-3154 ◽  
Author(s):  
Ryuji Ishizaki ◽  
Toshikazu Shinba ◽  
Go Mugishima ◽  
Hikaru Haraguchi ◽  
Masayoshi Inoue

Author(s):  
Patricia Cerrito ◽  
John Cerrito

The introduction of a time component requires the use of statistical methods that can utilize dependent data. The assumption of independence that is required for regression models is no longer applicable. In this section, we will work with time series analysis. Time series analysis requires that data are collected at discrete, fixed time intervals. Observational and insurance data contain time stamps as to the date of service. These time stamps are transactional in nature and do not occur at fixed time intervals. Therefore, the first step in such an analysis is to convert the transactional time points into fixed time intervals. We need to decide upon the interval: every minute, hour, day, week, month, year. The specific interval will depend upon the analysis to be performed. Once that is completed, the standard time series analysis methods can be used. As an example, we use the MEPS dataset for medications. We use the date of January 1 as time zero.


PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249589
Author(s):  
Yanguang Chen

A number of spatial statistic measurements such as Moran’s I and Geary’s C can be used for spatial autocorrelation analysis. Spatial autocorrelation modeling proceeded from the 1-dimension autocorrelation of time series analysis, with time lag replaced by spatial weights so that the autocorrelation functions degenerated to autocorrelation coefficients. This paper develops 2-dimensional spatial autocorrelation functions based on the Moran index using the relative staircase function as a weight function to yield a spatial weight matrix with a displacement parameter. The displacement bears analogy with the time lag in time series analysis. Based on the spatial displacement parameter, two types of spatial autocorrelation functions are constructed for 2-dimensional spatial analysis. Then the partial spatial autocorrelation functions are derived by using the Yule-Walker recursive equation. The spatial autocorrelation functions are generalized to the autocorrelation functions based on Geary’s coefficient and Getis’ index. As an example, the new analytical framework was applied to the spatial autocorrelation modeling of Chinese cities. A conclusion can be reached that it is an effective method to build an autocorrelation function based on the relative step function. The spatial autocorrelation functions can be employed to reveal deep geographical information and perform spatial dynamic analysis, and lay the foundation for the scaling analysis of spatial correlation.


2021 ◽  
Vol 13 (2) ◽  
pp. 302-328
Author(s):  
Hans H. Diebner ◽  
Nina Timmesfeld

Containment strategies to combat epidemics such as SARS-CoV-2/COVID-19 require the availability of epidemiological parameters, e.g., the effective reproduction number. Parametric models such as the commonly used susceptible-infected-removed (SIR) compartment models fitted to observed incidence time series have limitations due to the time-dependency of the parameters. Furthermore, fatalities are delayed with respect to the counts of new cases, and the reproduction cycle leads to periodic patterns in incidence time series. Therefore, based on comprehensible nonparametric methods including time-delay correlation analyses, estimates of crucial parameters that characterise the COVID-19 pandemic with a focus on the German epidemic are presented using publicly available time-series data on prevalence and fatalities. The estimates for Germany are compared with the results for seven other countries (France, Italy, the United States of America, the United Kingdom, Spain, Switzerland, and Brazil). The duration from diagnosis to death resulting from delay-time correlations turns out to be 13 days with high accuracy for Germany and Switzerland. For the other countries, the time-to-death durations have wider confidence intervals. With respect to the German data, the two time series of new cases and fatalities exhibit a strong coherence. Based on the time lag between diagnoses and deaths, properly delayed asymptotic as well as instantaneous fatality–case ratios are calculated. The temporal median of the instantaneous fatality–case ratio with time lag of 13 days between cases and deaths for Germany turns out to be 0.02. Time courses of asymptotic fatality–case ratios are presented for other countries, which substantially differ during the first half of the pandemic but converge to a narrow range with standard deviation 0.0057 and mean 0.024. Similar results are obtained from comparing time courses of instantaneous fatality–case ratios with optimal delay for the 8 exemplarily chosen countries. The basic reproduction number, R0, for Germany is estimated to be between 2.4 and 3.4 depending on the generation time, which is estimated based on a delay autocorrelation analysis. Resonances at about 4 days and 7 days are observed, partially attributable to weekly periodicity of sampling. The instantaneous (time-dependent) reproduction number is estimated from the incident (counts of new) cases, thus allowing us to infer the temporal behaviour of the reproduction number during the epidemic course. The time course of the reproduction number turns out to be consistent with the time-dependent per capita growth.


2020 ◽  

<p>Time series analysis is a very effective method to analyze the dynamic characteristics of soil moisture at long-term scale. In this study, we have used the time series to analyze the relationship between precipitation and soil moisture on fixed dune at different soil depths (from 0 to 120 cm) during the growing season (from May to September) of 2006-2010 in Korqin Sandy Land, northern China. The results indicate that: (1) The precipitation is a relatively independent time series and has no obvious autocorrelation. Precipitation in an earlier stage has no obvious effect on the later stage in the growing season. (2) Soil moisture in different soil layers has higher autocorrelation; and the autocorrelation of soil moisture in each soil layer is significantly weakened with the increase in time lag interval. (3) The correlation coefficient between soil moisture and precipitation in each soil layer is higher at the time lag interval of k = 0; with the increase in soil depth, the correlation is gradually weakened. (4) The maximum correlation coefficients of soil moisture series and precipitation series in different soil depths were obtained at the time lag interval of k = 0.</p>


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