Serie temporali

Vettori di dati campionati nel tempo

Le serie temporali rappresentano l’evoluzione del tempo di una popolazione o di un processo dinamico. Sono usati per identificare, modellare e prevedere pattern e comportamenti nei dati campionati su intervalli di tempo discreti.

Per questo tipo di dati, gli orari sono consigliati rispetto agli oggetti timeseries. Gli orari possono memorizzare dati temporali di vario tipo e dispongono di un'ampia serie di funzioni di supporto per allineare, abbinare ed eseguire calcoli. Per iniziare con gli orari, vedere Create Timetables.

Funzioni

espandi tutto

Creazione, modifica e plottaggio

`timeseries`	Create `timeseries` object
`timeseries2timetable`	Convert `timeseries` objects to timetable (Da R2021a)
`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

Query

`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

Statistiche descrittive

`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

Raccolte

`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`

Eventi

`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

Argomenti

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

Informazioni complementari

Orari