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In telemetry, commutation is a process whereby multiple data streams ("measurands"), possibly with differing data rates, are combined into a single frame-based stream for transmission, before being separated again (decommutated) upon reception; it is a form of time-division multiplexing. Frame synchronization must be achieved before a data ...
The dashed lines represent contours of the velocity field (streamlines), showing the motion of the whole field at the same time. (See high resolution version.) Solid blue lines and broken grey lines represent the streamlines. The red arrows show the direction and magnitude of the flow velocity. These arrows are tangential to the streamline.
Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. Generally, time series data is modelled as a stochastic process.
High frequency data employs the collection of a large sum of data over a time series, and as such the frequency of single data collection tends to be spaced out in irregular patterns over time. This is especially clear in financial market analysis, where transactions may occur in sequence, or after a prolonged period of inactivity.
The first is an example of processing a data stream using a continuous SQL query (a query that executes forever processing arriving data based on timestamps and window duration). This code fragment illustrates a JOIN of two data streams, one for stock orders, and one for the resulting stock trades.
In statistics, trend analysis often refers to techniques for extracting an underlying pattern of behavior in a time series which would otherwise be partly or nearly completely hidden by noise. If the trend can be assumed to be linear, trend analysis can be undertaken within a formal regression analysis, as described in Trend estimation.
Statistics is a field of inquiry that studies the collection, analysis, interpretation, and presentation of data. It is applicable to a wide variety of academic disciplines , from the physical and social sciences to the humanities ; it is also used and misused for making informed decisions in all areas of business and government .
In many cases, the repositories of time-series data will utilize compression algorithms to manage the data efficiently. [ 3 ] [ 4 ] Although it is possible to store time-series data in many different database types, the design of these systems with time as a key index is distinctly different from relational databases which reduce discrete ...