analyzeTS {CoSMoS} | R Documentation |
The Functions analyzeTS, reportTS, and simulateTS
Description
Provide a complete set of tools to make time series analysis a piece of cake -
analyzeTS
automatically performs seasonal analysis, fits distributions
and correlation structures, reportTS
provides visualizations of the fitted
distributions and correlation structures, and a table with the values of the fitted
parameters and basic descriptive statistics, simulateTS
automatically takes
the results of analyzeTS
and generates synthetic ones.
Usage
analyzeTS(
TS,
season = "month",
dist = "ggamma",
acsID = "weibull",
norm = "N1",
n.points = 30,
lag.max = 30,
constrain = FALSE,
opts = NULL
)
reportTS(aTS, method = "dist")
simulateTS(aTS, from = NULL, to = NULL)
Arguments
TS |
time series in format - date, value |
season |
name of the season (e.g. month, week) |
dist |
name of the distribution to be fitted |
acsID |
ID of the autocorrelation structure to be fitted |
norm |
norm used for distribution fitting - id ('N1', 'N2', 'N3', 'N4') |
n.points |
number of points to be subsetted from ecdf |
lag.max |
max lag for the empirical autocorrelation structure |
constrain |
logical - constrain shape2 parametes for finite tails |
opts |
minimization options |
aTS |
analyzed timeseries |
method |
report method - |
from |
starting date/time of the simulation |
to |
end date/time of the simulation |
Details
In practice, we usually want to simulate a natural process using some sampled time series.
To generate a synthetic time series with similar characteristics to the observed values,
we have to determine marginal distribution, autocorrelation structure and probability zero
for each individual month. This can is done by fitting distributions and autocorrelation
structures with analyzeTS
. Result can be checked with reportTS
.
Syynthetic time series with the same statistical properties can be produced with
simulateTS
.
Recomended distributions for variables:
-
precipitation: ggamma (Generalized Gamma), burr### (Burr type)
-
streamflow: ggamma (Generalized Gamma), burr### (Burr type)
-
relative humidity: beta
-
temperature: norm (Normal distribution)
Examples
library(CoSMoS)
## Load data included in the package
## (to find out more about the data use ?precip)
data('precip')
## Fit seasonal ACSs and distributions to the data
a <- analyzeTS(precip)
reportTS(a, 'dist') ## show seasonal distribution fit
reportTS(a, 'acs') ## show seasonal ACS fit
reportTS(a, 'stat') ## display basic descriptive statisctics
######################################
## 'duplicate' analyzed time series ##
sim <- simulateTS(a)
## plot the result
precip[, id := 'observed']
sim[, id := 'simulated']
dta <- rbind(precip, sim)
ggplot(dta) +
geom_line(aes(x = date, y = value)) +
facet_wrap(~id, ncol = 1) +
theme_classic()
################################################
## or simulate timeseries of different length ##
sim <- simulateTS(a,
from = as.POSIXct('1978-12-01 00:00:00'),
to = as.POSIXct('2008-12-01 00:00:00'))
## and plot the result
precip[, id := 'observed']
sim[, id := 'simulated']
dta <- rbind(precip, sim)
ggplot(dta) +
geom_line(aes(x = date, y = value)) +
facet_wrap(~id, ncol = 1) +
theme_classic()