**Elenco dei comandi di R per l’analisi delle serie storiche**

Si riportano di seguito le principali funzioni di R utili per l’analisi delle serie storiche contenute nei packages: stats, tseries, ast e lmtest. Altri comandi, che esulano dalla trattazione del presente lavoro, possono essere reperiti nei package dse1 e dse2 (Multivariate Time Series Library), its (Irregular Time Series), fBasics, fOptions , fExtremes e fSeries (Financial Software Collection).

**INPUT**

cycle(): gives the positions in the cycle of each observation (stats)

deltat(): returns the time interval between observations (stats)

end(): extracts and encodes the times the last observation were taken (stats)

frequency(): returns the number of samples per unit time (stats)

read.ts(): reads a time series file (tseries)

start(): extracts and encodes the times the first observation were taken (stats)

time(): creates the vector of times at which a time series was sampled (stats)

ts(): creates time-series objects (stats)

window(): is a generic function which extracts the subset of the object 'x' observed between the times 'start' and 'end'. If a frequency is specified, the series is then re-sampled at the new frequency (stats)

**TS DECOMPOSITION**

decompose(): decomposes a time series into seasonal, trend and irregular components using moving averages. Deals with additive or multiplicative seasonal component (stats)

filter(): linear filtering on a time series (stats)

HoltWinters(): computes Holt-Winters Filtering of a given time series (stats)

sfilter(): removes seasonal fluctuation using a simple moving average (ast)

spectrum(): estimates the spectral density of a time series (stats)

stl(): decomposes a time series into seasonal, trend and irregular components using 'loess' (stats)

tsr(): decomposes a time series into trend, seasonal and irregular. Deals with additive and multiplicative components (ast)

**TESTS**

adf.test(): computes the Augmented Dickey-Fuller test for the null that 'x' has a unit root (tseries)

Box.test(): computes the Box-Pierce or Ljung-Box test statistic for examining the null hypothesis of independence in a given time series (stats)

bds.test(): computes and prints the BDS test statistic for the null that 'x' is a series of i.i.d. random variables (tseries)

bptest(): performs the Breusch-Pagan test for heteroskedasticity of residuals (lmtest)

dwtest(): performs the Durbin-Watson test for autocorrelation of residuals (lmtest)

jarque.bera.test(): Jarque-Bera test for normality (tseries)

kpss.test(): computes KPSS test for stationarity (tseries)

shapiro.test(): Shapiro-Wilk Normality Test (stats)

**STOCHASTIC MODELS**

ar(): fits an autoregressive time series model to the data, by default selecting the complexity by AIC (stats)

arima(): fits an ARIMA model to a univariate time series (stats)

arima.sim(): simulate from an ARIMA model (stats)

arma(): fits an ARMA model to a univariate time series by conditional least squares (tseries)

garch(): fits a Generalized Autoregressive Conditional Heteroscedastic GARCH(p, q) time series model to the data by computing the maximum-likelihood estimates of the conditionally normal model (tseries)

**GRAPHICS**

lag.plot: plots time series against lagged versions of themselves. Helps visualizing "auto-dependence" even when auto-correlations vanish (stats)

monthplot(): plots a seasonal (or other) subseries of a time series (stats)

plot.ts(): plotting time-series objects (stats)

seaplot(): plotting seasonal sub-series or profile (ast)

seqplot.ts(): plots a two time series on the same plot frame (tseries)

tsdiag(): a generic function to plot time-series diagnostics (stats)

ts.plot(): plots several time series on a common plot. Unlike 'plot.ts' the series can have a different time bases, but they should have the same frequency (stats)

**MISCELLANEOUS**

acf(), pacf(), ccf(): the function 'acf' computes (and by default plots) estimates of the autocovariance orautocorrelation function. Function 'pacf' is the function used for the partial autocorrelations. Function 'ccf' computes the cross-correlation or cross-covariance of two univariate series (stats)

diff.ts(): returns suitably lagged and iterated differences (stats)

lag(): computes a lagged version of a time series, shifting the time base back by a given number of observations (stats)

Vito Ricci

**Successivo: **Riferimenti

**Sommario: **Index