In regular time series, the values have a defined start and end time (temporalExtent) and a constant time increment (timeInterval).
We have defined start and end times to our working days.
In such an approach, unlike the classical one based on stochastic processes, statistical functions and probability concepts are defined starting from a single observed time series instead of an ensemble of realizations of a stochastic process.
Event signals define the start time, the duration, the periodicity for periodic events, and the numbers of repetitions.
Without loss of generality, we define the start time of node s's broadcast to be slot zero, and the broadcast latency is the maximum time taken by a message to reach all nodes.
Therefore, we calculate 'the' marginal effect of a given programme as an average over several typical variants of the programme defined by starting time and duration.
For convenience, in the remainder of this paper, the start of a LAOS sequence is defined as starting time i.e., \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$t=0$$\end{document} t = 0.
Time of day was defined as the start time of the cleaning event considered in analysis.
The time series were defined by the start time and the time resolution of the measurements (Fig. 3).
We also tested another coda window in which we define the starting time based on inter-station distances and surface wave velocity (dashed gray lines in Fig. 2).
For every request, the client defines the source, destination, start time and duration of the connection.
