Séries temporelles

Vecteurs de données échantillonnés au fil du temps

Les séries temporelles représentent l’évolution d’une population ou d’un processus dynamique au fil du temps. Elles permettent d’identifier, de modéliser et de prévoir des patterns et des comportements dans les données sur des intervalles de temps discrets.

Il est recommandé d’utiliser des timetables plutôt que des objets timeseries pour ce type de données. Les timetables peuvent stocker divers types de données horodatées et proposent un large choix de fonctions associées permettant d’aligner et de combiner des timetables et d’effectuer des calculs. Pour en savoir plus sur les timetables, consultez Create Timetables.

Fonctions

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Créer, modifier et tracer

`timeseries`	Create `timeseries` object
`timeseries2timetable`	Convert `timeseries` objects to timetable (depuis R2021b)
`resample`	Resample time vector in `timeseries` or `tscollection`
`synchronize`	Synchronize and resample two `timeseries` objects using common time vector
`detrend`	Subtract mean or best-fit line from `timeseries` object
`filter`	Modify frequency content of `timeseries` objects
`idealfilter`	`timeseries` ideal filter
`plot`	Plot `timeseries`
`append`	Concatenate `timeseries` objects in time
`addsample`	Add data sample to `timeseries` object
`delsample`	Remove sample from `timeseries` object
`setabstime`	Set `timeseries` or `tscollection` times as date character vectors
`setinterpmethod`	Set default interpolation method for `timeseries` object
`setuniformtime`	Modify uniform `timeseries` time vector

Requêtes

`getsamples`	Subset of `timeseries`
`getsampleusingtime`	Subset of `timeseries` or `tscollection` data
`getdatasamples`	Access `timeseries` data samples
`getdatasamplesize`	`timeseries` data sample size
`getabstime`	Convert `timeseries` or `tscollection` time vector to cell array
`getinterpmethod`	`timeseries` interpolation method
`getqualitydesc`	`timeseries` data quality

Statistiques descriptives

`iqr`	Interquartile range of `timeseries` data
`max`	Maximum of `timeseries` data
`mean`	Mean of `timeseries` data
`median`	Median of `timeseries` data
`min`	Minimum of `timeseries` data
`std`	Standard deviation of `timeseries` data
`sum`	Sum of `timeseries` data
`var`	Variance of `timeseries` data

Collections

`tscollection`	Create `tscollection` object
`addts`	Add `timeseries` to `tscollection`
`removets`	Remove `timeseries` from `tscollection`
`addsampletocollection`	Add sample to `tscollection`
`delsamplefromcollection`	Delete sample from `tscollection`
`gettimeseriesnames`	Names of `timeseries` in `tscollection`
`settimeseriesnames`	Rename `timeseries` in `tscollection`

Événements

`tsdata.event`	Create `tsdata.event` object
`addevent`	Add event to `timeseries`
`delevent`	Remove event from `timeseries`
`findEvent`	Query `tsdata.event` by name
`getTimeStr`	Query `tsdata.event` times
`gettsafteratevent`	Create `timeseries` at or after event
`gettsafterevent`	Create `timeseries` after event
`gettsatevent`	Create `timeseries` at event
`gettsbeforeatevent`	Create `timeseries` at or before event
`gettsbeforeevent`	Create `timeseries` before event
`gettsbetweenevents`	Create `timeseries` between events

Rubriques

Time Series Objects and Collections
There are two types of time series objects in MATLAB^®, a timeseries object and a tscollection object.

Informations connexes

Timetables