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In probability theory and statistics, a continuous-time stochastic process, or a continuous-space-time stochastic process is a stochastic process for which the index variable takes a continuous set of values, as contrasted with a discrete-time process for which the index variable takes only distinct values. An alternative terminology uses continuous parameter as being more inclusive.cite-ref-1[1]

A more restricted class of processes are the continuous stochastic processes; here the term often (but not alwayscite-ref-d-2-0[2]) implies both that the index variable is continuous and that sample paths of the process are continuous. Given the possible confusion, caution is needed.cite-ref-d-2-1[2]

Continuous-time stochastic processes that are constructed from discrete-time processes via a waiting time distribution are called continuous-time random walks.cite-ref-3[3]

• Examples

• See also

• References

An example of a continuous-time stochastic process for which sample paths are not continuous is a Poisson process. An example with continuous paths is the Ornstein–Uhlenbeck process.

• Continuous signal

cite-note-11. ↑ Parzen, E. (1962) Stochastic Processes, Holden-Day. ISBN 0-8162-6664-6 (Chapter 6)

cite-note-d-22. ↑ Dodge, Y. (2006) The Oxford Dictionary of Statistical Terms, OUP. ISBN 0-19-920613-9 (Entry for "continuous process")

cite-note-33. ↑ citerefpaulbaschnagel2013Paul, Wolfgang; Baschnagel, Jörg (2013-07-11). Stochastic Processes: From Physics to Finance. Springer Science & Business Media. pp. 72–74. ISBN 9783319003276. Retrieved 20 June 2022.