autocorrelation


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autocorrelation

(ˌɔːtəʊˌkɒrɪˈleɪʃən)
n
(Statistics) the condition occurring when successive items in a series are correlated so that their covariance is not zero and they are not independent. Also called: serial correlation
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The aim of this article is to present simple and effective reliable methods for fault diagnosis of inner race bearing defect based on optimal Morlet wavelet filter to raw signal and autocorrelation enhancement.
At closer inspection of the kernel autocorrelation, it is evident that it is the same shape, and this is the second observation.
[mathematical expression not reproducible] of autocorrelation noise reduction and morphological filtering.
It is not possible to understand the modeling of data over time without first understanding the concept of autocorrelation. Autocorrelation is a coefficient of correlation that measures the intensity of the relationship of the series with itself.
The result showed a positive spatial autocorrelation in percent change in the CYP 2015-16, compared to 2014-15, however it was not statistically significant (Moran's I = 0.0813573, p-value = of 0.074).
Unlike [10], [12], where the kurtosis test is only considered, the separated signals are tested using different spectrum sensing algorithms a Goodness of Fit (GoF) [13] and autocorrelation (AC) tests [14] will be applied on the separated signals.
The stationary can be determined by looking at autocorrelation function (ACF) and partial autocorrelation function (PACF).
After picking a good performing model, based on the set criteria (autocorrelation, cross-correlation), it remains to be seen whether the selected model is good enough for another data set.
The Binary Offset Carrier (BOC) signal is used in the global navigation satellite system (GNSS) and is characterized by multiple peaks in its autocorrelation function and spectrum splitting [1-3].
The authors proposed a vehicle-pedestrian accident data analysis model based on spatial autocorrelation analysis to identify and sort unsafe bus stops and generate hot spots map for vehicles and pedestrian accidents [20].
Geographically weighted regression was necessary to create best-fitting models due to spatial autocorrelation and nonstationarity.
In addition, as the time interval becomes smaller, the presence of autocorrelation in the data increases, which impacts savings uncertainty and necessitates its assessment.