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Spatial-Temporal Patterns of Gender Inequalities in University Enrollment in Nigeria:
Published in Esra Ozdenerol, Gender Inequalities, 2021
Moses O. Olawole, Akanni I. Akinyemi, Olayinka A. Ajala
In general, the values of Global Moran’s I would be between −1 and +1 (Goodchild 1986). Negative autocorrelation values (–1) mean nearby locations tended to have dissimilar values; positive autocorrelation values (+1) mean that similar values tended to occur in adjacent areas; and values close to zero indicate no spatial autocorrelation (Goodchild 1986). Along with the index, Z-scores are usually reported for the statistical significance test. If Z is out of ±1.96, the null hypothesis of the randomness test is rejected at the 95% confidence level, which means the pattern is spatially auto-correlated (Gao et al. 2016). The Global Moran’s I was computed to identify clustering (spatial autocorrelation) in gender disparity in university enrollment in the country.
Data Statistics and Analytics
Published in Paresh Chra Deka, A Primer on Machine Learning Applications in Civil Engineering, 2019
Autocorrelation refers to the correlation of a time series with its own past and future values. Autocorrelation is sometimes called ‘serial correlation,’ which refers to the correlation between members of a series with numbers arranged in time. Autocorrelation is just one measure of randomness. It complicates the application of statistical tests by reducing the effective sample size and the identification of significant covariance or correlation between time series. The randomness is ascertained by computing autocorrelation for data values at varying time lags. If random, such autocorrelations should be near zero for any and all time-lag separations. If non-random, then one or more of the autocorrelations will be significantly non-zero. Checking for autocorrelation is typically a sufficient test of randomness as the residuals from poor fitting models tend to display non-subtle randomness. But some applications require a more rigorous determination of randomness.
Working with Profile Data
Published in Max Kuhn, Kjell Johnson, Feature Engineering and Selection, 2019
These intricate relationships among wavelengths within a day and bioreactor can be observed through a plot of the autocorrelations. An autocorrelation is the correlation between the original series and each sequential lagged version of the series. In general, the autocorrelation decreases as the lag value increases, and can increase at later lags with trends in seasonality. The autocorrelation plot for the wavelengths of the first bioreactor and several days are displayed in Figure 9.4 (a) (which is representative of the relationships among wavelengths across other bioreactor and day combinations). This figure indicates that the correlations between wavelengths are different across days. Later days tend to have higher between-wavelength correlations but, in all cases, it takes many hundreds of lags to get the correlations below zero.
Maintenance scheduling using data mining techniques and time series models
Published in International Journal of Management Science and Engineering Management, 2018
Payam Gholami, Ashkan Hafezalkotob
where yt and at are the actual value and random error at time period t respectively, and , where the B is the backward shift operator. and are defined as the model parameters and ∇ = (1 – B). Finally, d is an integer and often is referred as order of differencing; p and q are integers and usually are referred as orders of the model. Random errors, at, are assumed to be identically distributed with zero mean and a constant variance. If a time series is produced from an ARIMA process, it should have some theoretical autocorrelation characteristics. Detection of potential models for the specified time series is possible by a number of model identification methods. The most commonly-used method fits an accurate model by comparing the theoretical autocorrelation patterns with its empirical counterpart.
Applying human mobility and water consumption data for short-term water demand forecasting using classical and machine learning models
Published in Urban Water Journal, 2020
Kamil Smolak, Barbara Kasieczka, Wieslaw Fialkiewicz, Witold Rohm, Katarzyna Siła-Nowicka, Katarzyna Kopańczyk
We use the autocorrelation function (ACF) and the partial autocorrelation function (PACF) for an initial determination of ARIMA and ARIMAX orders and validation of results. Autocorrelation is defined as the correlation between an element of a signal with its lag. Therefore, the autocorrelation of lag is a correlation between elements and . Partial autocorrelation is a correlation between a signal with its own lagged values, where linear dependence for shorter lags are removed.
A Modular Real-time Tidal Prediction Model based on Grey-GMDH Neural Network
Published in Applied Artificial Intelligence, 2018
Ze-Guo Zhang, Jian-Chuan Yin, Cheng Liu
The concept of correlation analysis (Young and Shellswell 1972) originates from signal processing and analysis; correlation analysis reflects how the correlation between any two values in a time series is changed over time. In addition, autocorrelation analysis depicts the correlation between neighboring variables of the time series. Autocorrelation function and partial autocorrelation function are an effective way of analyzing and dealing with complex time series (Nezli and Li 2003; Zhao et al. 2014): the autocorrelation function describes the relationship between the adjacent variables of time series, whereas the partial correlation function eliminates the influence of other intermediate variables in time series. In tidal data analysis, the time series of the tidal level is affected by many variable factors that are difficult to be measured by nautical equipment, thereby making it difficult to calculate the contribution to tidal level. Consequently, the correlation analysis method is utilized to analyze the correlation between the time series of tidal level and then to determine the input dimension of the Grey-GMDH prediction model. In this study, a correlation value of 0.5 is selected as a reference standard to determine the input dimension, and the analysis results are shown in Figures 4 and 5. Figure 4 shows that the autocorrelation coefficient of the tidal-level time-series data is tailing. Meanwhile, the autocorrelation coefficient reaches gradually closer to zero and tends to be stable. Furthermore, Figure 5 shows that the partial autocorrelation coefficient is four-order truncation. To summarize, the correlation analysis demonstrates that the time-series value from t-1 to t-4 moment has a significant relevance with the time-series value of moment t, which can be selected as the input of the modular prediction model.