The LabVIEW Time Series Analysis Tools provide a collection of VIs that assists you in analyzing scientific and engineering time series and rapidly deploying engineering applications based on the analysis results. You can use these VIs to handle discrete univariate and multivariate (vector) time series.
The following diagram illustrates the procedure that you can follow when using the Time Series Analysis Tools to analyze a time series.
A typical time series analysis procedure includes the following steps:
The Time Series Analysis Tools categorize time series analysis into the following methods:
Each of the time series analysis methods is classified as either time domain or frequency domain. You can select appropriate methods from these two classes according to the analysis objective.
All of the statistical analysis methods, the modeling and prediction methods, and the correlation methods discussing in this help are time-domain methods. You can use the statistical analysis methods to investigate the stochastic characteristics of a time series. Stochastic characteristics, for example, are helpful in quality controls in manufacturing production. If you have two or more related time series, you can analyze them jointly using a covariance matrix, PCA, or cross-correlation method to investigate their relatedness. ICA can separate independent signals from linearly mixed data. The modeling methods help you build parametric behavioral models for time series, which help you predict or control future values.
The spectral analysis methods are frequency-domain methods. You can use the nonparametric or model-based spectral analysis methods to investigate the vibration characteristics of physical systems, such as resonance frequencies and harmonic frequencies. Some of the methods also support multivariate time series, such as the MUSIC method, which computes the common spectral components existing in a multivariate vibration time series.
Before applying an analysis method to a time series, you need to preprocess the signal. For example, you need to make sure that the signal contains no low-frequency trends, that the frequency bandwidth is sufficiently narrow, and that the sampling rate is sufficiently high. Use the Preprocessing VIs to preprocess a time series.
The Time Series Analysis Tools also provide a group of Utilities VIs that you can use to generate time series samples, to scale to an engineering unit, to average the power spectrum, or to load pre-stored data from a file.