Package: itsmr 1.10

George Weigt

itsmr: Time Series Analysis Using the Innovations Algorithm

Provides functions for modeling and forecasting time series data. Forecasting is based on the innovations algorithm. A description of the innovations algorithm can be found in the textbook "Introduction to Time Series and Forecasting" by Peter J. Brockwell and Richard A. Davis. <https://link.springer.com/book/10.1007/b97391>.

Authors:George Weigt

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itsmr.pdf |itsmr.html✨
itsmr/json (API)

# Install 'itsmr' in R:
install.packages('itsmr', repos = c('https://georgeweigt.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/georgeweigt/itsmr-refman.pdf/issues

On CRAN:

2.34 score 217 scripts 656 downloads 74 exports 0 dependencies

Last updated 2 years agofrom:8312b117d0. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-winOKOct 31 2024
R-4.5-linuxOKOct 31 2024
R-4.4-winOKOct 31 2024
R-4.4-macOKOct 31 2024
R-4.3-winOKOct 31 2024
R-4.3-macOKOct 31 2024

Exports:.difference_sign_test.fcomp.forecast.arma.forecast.diff.forecast.hr.forecast.log.forecast.season.forecast.transform.forecast.trend.innovation.kernel.innovation.update.innovations.ljung_box_test.mcleod_li_test.plot.forecast.rank_test.test.aacvf.test.acvf.test.ar.inf.test.arar.deaths.test.arar.kernel.test.arar.trings.test.forecast.deaths.test.forecast.dowj.test.forecast.kernel.test.forecast.lake.test.forecast.sunspots.test.forecast.wine.test.ia.test.ma.inf.test.out.test.periodogram.test.smooth.exp.test.smooth.fft.test.smooth.ma.trings.turning_point_test.yw.origaacvfacvfairpassar.infarararmaautofitburgcheckdeathsdowjforecasthannanhrialakema.infperiodogramplotaplotcplotsResidseasonselftestsimsmooth.expsmooth.fftsmooth.masmooth.rankspecifystrikesSunspotstesttrendwineyw

Dependencies:

Readme and manuals

Help Manual

Help pageTopics
Time Series Analysis Using the Innovations Algorithmitsmr-package itsmr
Autocovariance of ARMA modelaacvf
Autocovariance of dataacvf
Number of international airline passengers, 1949 to 1960airpass
Compute AR infinity coefficientsar.inf
Forecast using ARAR algorithmarar
Estimate ARMA model coefficients using maximum likelihoodarma
Find the best model from a range of possible ARMA modelsautofit
Estimate AR coefficients using the Burg methodburg
Check for causality and invertibilitycheck
USA accidental deaths, 1973 to 1978deaths
Dow Jones utilities index, August 28 to December 18, 1972dowj
Forecast future valuesforecast
Estimate ARMA coefficients using the Hannan-Rissanen algorithmhannan
Estimate harmonic componentshr
Estimate MA coefficients using the innovations algorithmia
Level of Lake Huron, 1875 to 1972lake
Compute MA infinity coefficientsma.inf
Plot a periodogramperiodogram
Plot data and/or model ACF and PACFplota
Plot one or two time seriesplotc
Plot spectrum of data or ARMA modelplots
Compute residualsResid
Estimate seasonal componentseason
Run a self testselftest
Generate synthetic observationssim
Apply an exponential filtersmooth.exp
Apply a low pass filtersmooth.fft
Apply a moving average filtersmooth.ma
Apply a spectral filtersmooth.rank
Specify an ARMA modelspecify
USA union strikes, 1951-1980strikes
Number of sunspots, 1770 to 1869Sunspots
Test residuals for stationarity and randomnesstest
Estimate trend componenttrend
Australian red wine sales, January 1980 to October 1991wine
Estimate AR coefficients using the Yule-Walker methodyw