Sentence examples for time series data analyzed from inspiring English sources

The data set contains daily time series data analyzed and recorded at the WWTP plant during the period June Sept 2014 with a total of 88 data points (period of 4 months) of seven variables, namely pH, total solids (TS), chemical oxygen demand (COD), temperature (T), free ammonia (FA), ammonia nitrogen (AN) and total Kjeldahl Nitrogen (TKN).

Metabolite time-series data analyzed in a stimulus response experiment revealed substantial changes in the concentrations of intermediates, and were used for identifying network dynamics.

Time series data were analyzed using a multivariable general linear model (GLM).

fMRI time series data were analyzed using a general linear model to determine the presence of regional IED-related BOLD changes in SPM5 (spm.fil.ac.uk).ac.uk

Time series data were analyzed by using a generalized linear model using a normal distribution and identity-link function and were extended by generalized estimating equations (GEEs) with an autoregressive process of the first order to handle repeated observations within a subject [ 20].

The power spectral density (PSD) is computed for the time series data to analyze the disturbance rejection as the function of frequency and shown in Figure 13.

Most time series data is currently analyzed using methods that were initially developed for static data, and while these methods have provided biologically meaningful insights, they assume that each of the data points is independent [16] [19].

Time series data is often analyzed using principal component analysis or partial least squares regression, e.g., Janes et al [ 7- 9].

Time series data are often analyzed using standard clustering algorithms such as hierarchical clustering, k-means and self-organizing maps [ 20- 22].

The input to Wigwams is a matrix of gene expression values for all the time series data to be analyzed with unique gene identifiers and annotation of each time series sample.

The detection of bubbles was possible by denoising local time series data and analyzing for its intermittent behavior.