This allows users to visually inspect the data for recurring patterns, overlaps and gaps in interaction. They are typically used to analyze the flow of topics under discussion, the use of artifacts, and the participation and conversational flow of the subjects. Video analysis and interpretation may also be assisted through the use of more formal models and analysis methods. In time series analysis, the time series is presumed to include sequential dependencies, such that the value of the current observation is influenced by the values of prior observations. Analysis in the frequency domain is sometimes referred to as spectral analysis and consists of a Fourier decomposition of the time series into a sum of sine waves.
Spectral analysis is used when the data is generated by wave-like, or seasonal, processes.
Autocorrelations and lagged correlations provide a way of detecting sequential dependencies but they provide little information about the causes of those dependencies. Often, processes can be modeled in terms of both inputs and outputs. They also assume that the present input will have a greater impact on the present output than any earlier input. The times series methods mentioned above all seek to capture statistical relationships between sequences of numbers. In other cases the output of a process may be a set of events that can be classified or categorized, but not numerically quantified.