Detecting Causalities in Production Environments Using Time Lag Identification with Cross-Correlation in Production State Time Series

Author(s):  
Dirk Saller ◽  
Bora I. Kumova ◽  
Christoph Hennebold
2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Gadi Goelman ◽  
Rotem Dan ◽  
Filip Růžička ◽  
Ondrej Bezdicek ◽  
Evžen Růžička ◽  
...  

Abstract We describe an analysis method that characterizes the correlation between coupled time-series functions by their frequencies and phases. It provides a unified framework for simultaneous assessment of frequency and latency of a coupled time-series. The analysis is demonstrated on resting-state functional MRI data of 34 healthy subjects. Interactions between fMRI time-series are represented by cross-correlation (with time-lag) functions. A general linear model is used on the cross-correlation functions to obtain the frequencies and phase-differences of the original time-series. We define symmetric, antisymmetric and asymmetric cross-correlation functions that correspond respectively to in-phase, 90° out-of-phase and any phase difference between a pair of time-series, where the last two were never introduced before. Seed maps of the motor system were calculated to demonstrate the strength and capabilities of the analysis. Unique types of functional connections, their dominant frequencies and phase-differences have been identified. The relation between phase-differences and time-delays is shown. The phase-differences are speculated to inform transfer-time and/or to reflect a difference in the hemodynamic response between regions that are modulated by neurotransmitters concentration. The analysis can be used with any coupled functions in many disciplines including electrophysiology, EEG or MEG in neuroscience.


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.


2021 ◽  
Vol 13 (6) ◽  
pp. 1193
Author(s):  
Zhongtian Ma ◽  
Hok Sum Fok ◽  
Linghao Zhou

Estuarine freshwater transport has a substantial impact on the near-shore ecosystem and coastal ocean environment away from the estuary. This paper introduces two independent methods to track the Mekong freshwater-induced mass transport by calculating the time lag (or equivalently, the phase) between in situ Mekong basin runoff and the equivalent water height (EWH) time series over the western South China Sea from a gravity recovery and climate experiment (GRACE). The first method is the harmonic analysis that determines the phase difference between annual components of the two time series (called the P-method), and the other is the cross-correlation analysis that directly obtains the time lag by shifting the lagged time series forward to attain the highest cross-correlation between the two time series (called the C-method). Using a three-year rolling window, the time lag variations in three versions of GRACE between 2005 and 2012 are computed for demonstrating the consistency of the results. We found that the time lag derived from the P-method is, on average, slightly larger and more variable than that from the C-method. A comparison of our gridded time lag against the age determined via radium isotopes in September, 2007 by Chen et al. (2010) revealed that our gridded time lag results were in good agreement with most isotope-derived ages, with the largest difference less than 6 days. Among the three versions of the GRACE time series, CSR Release 05 performed the best. The lowest standard deviation of time lag was ~1.6 days, calculated by the C-method, whereas the mean difference for all the time lags from the isotope-derived ages is ~1 day by P-method. This study demonstrates the potential of monitoring Mekong estuarine freshwater transport over the western South China Sea by GRACE.


2007 ◽  
Vol 22 (2) ◽  
pp. 113-126 ◽  
Author(s):  
V. Monbet ◽  
P. Ailliot ◽  
M. Prevosto

2011 ◽  
Vol 390 (9) ◽  
pp. 1677-1683 ◽  
Author(s):  
G.F. Zebende ◽  
P.A. da Silva ◽  
A. Machado Filho

2020 ◽  
Vol 94 ◽  
Author(s):  
A.L. May-Tec ◽  
N.A. Herrera-Castillo ◽  
V.M. Vidal-Martínez ◽  
M.L. Aguirre-Macedo

Abstract We present a time series of 13 years (2003–2016) of continuous monthly data on the prevalence and mean abundance of the trematode Oligogonotylus mayae for all the hosts involved in its life cycle. We aimed to determine whether annual (or longer than annual) environmental fluctuations affect these infection parameters of O. mayae in its intermediate snail host Pyrgophorus coronatus, and its second and definitive fish host Mayaheros urophthalmus from the Celestun tropical coastal lagoon, Yucatan, Mexico. Fourier time series analysis was used to identify infection peaks over time, and cross-correlation among environmental forcings and infection parameters. Our results suggest that the transmission of O. mayae in all its hosts was influenced by the annual patterns of temperature, salinity and rainfall. However, there was a biannual accumulation of metacercarial stages of O. mayae in M. urophthalmus, apparently associated with the temporal range of the El Niño-Southern Oscillation (five years) and the recovery of the trematode population after a devasting hurricane. Taking O. mayae as an example of what could be happening to other trematodes, it is becoming clear that environmental forcings acting at long-term temporal scales affect the population dynamics of these parasites.


2017 ◽  
Vol 362 (7) ◽  
Author(s):  
Songpeng Pei ◽  
Guoqiang Ding ◽  
Zhibing Li ◽  
Yajuan Lei ◽  
Rai Yuen ◽  
...  

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