Difference between revisions of "Temporal Concentration"
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'''Temporal Concentration''' (also Temporal Clustering) describes dynamic (time dependent) phenomena where the occurrence (rate, frequency or other measured quantity) of events exhibits non-uniform characteristics | '''Temporal Concentration''' (also Temporal Clustering) describes dynamic (time dependent) phenomena where the occurrence (rate, frequency or other measured quantity) of events exhibits non-uniform characteristics | ||
| − | == | + | == Models == |
| − | The archetype of a temporal process that does not exhibit concentration or clustering of events is the [[wikipedia:Poisson process]]. A variety of other proposed [[wikipedia:Point process]] can be used to model temporal concentration. | + | The archetype of a temporal process that ''does not'' exhibit concentration or clustering of events is the [[wikipedia:Poisson process | Poisson process]]. A variety of other proposed [[wikipedia:Point process | point processes]] can be used to model temporal concentration. |
| + | |||
| + | A special class of point process that exhibits enhanced clustering is the ''Hawkes process'' (also known as a self-exciting counting process). It is a simple point process but whose conditional intensity depends on the previous even count (hence the occurence of an event may precipitate more events). | ||
| + | |||
| + | == Measurement == | ||
| + | * Binning of temporal intervals (e.g. hourly, daily, monthly etc) | ||
| + | * Aggregation of amounts or counts within interval | ||
| + | * Application of standard [[Univariate Concentration Index]] | ||
== See Also == | == See Also == | ||
Revision as of 22:47, 14 June 2021
Contents
Definition
Temporal Concentration (also Temporal Clustering) describes dynamic (time dependent) phenomena where the occurrence (rate, frequency or other measured quantity) of events exhibits non-uniform characteristics
Models
The archetype of a temporal process that does not exhibit concentration or clustering of events is the Poisson process. A variety of other proposed point processes can be used to model temporal concentration.
A special class of point process that exhibits enhanced clustering is the Hawkes process (also known as a self-exciting counting process). It is a simple point process but whose conditional intensity depends on the previous even count (hence the occurence of an event may precipitate more events).
Measurement
- Binning of temporal intervals (e.g. hourly, daily, monthly etc)
- Aggregation of amounts or counts within interval
- Application of standard Univariate Concentration Index
