StatTimeSerAnalysis Namespace

Time series analysis.

Introduces several routines for handling/analyzing univariante time series. Includes ARMA, ARIMA and exponential smoothing routines.

As stated at NIST pages, time series is an ordered sequence of values of a variable at equally spaced time intervals. The usage of time series models is twofold:

Obtain an understanding of the underlying forces and structure that produced the observed data.
Fit a model and proceed to forecasting, monitoring or even feedback and feedforward control.

The fitting of time series models can be an ambitious undertaking. This unit utilizes the following:

average smoothing,
Holt-Winters single, double and triple exponential smoothing,
ARMA and ARIMA models,
ARAR model.

Literature used

Brockwell, P.J. and Davis, R.A. : Introduction to Time Series and Forecasting - second edition, Springer Verlag, New York, 2002.
Brockwell, P.J. and Davis, R.A. : Time Series: Theory and Methods - second edition, Springer Verlag, New York, 1991.
Shumway, R.H. and Stoffer, D.S. : Time Series Analysis and Its Applications, Springer Verlag, New York, 2000.
hrefhttp://www.stat.unc.edu/faculty/hurd/progswww.stat.unc.edu/faculty/hurd/progs
hrefhttp://www.itl.nist.gov/div898/handbook/pmc/section4/pmc43.htmeww.itl.nist.gov/div898/handbook/pmc/section4/pmc43.htm.
hrefhttp://www.it.iitb.ac.in/~praj/acads/seminar/04329008_ExponentialSmoothing.pdfwww.it.iitb.ac.in/~praj/acads/seminar/04329008_ExponentialSmoothing.pdf.

Topics

Name	Description
Functions	The following table lists functions in this documentation.
Structs, Records, Enums	The following table lists structs, records, enums in this documentation.

Functions

	Name	Description
	ACF	Autocorrelation/autocovariance function.
	ARARFit	Fit ARAR algorithm.
	ARARForecast	Forecast time series by ARAR.
	ARBurgFit	Burg AR estimation.
	ARIMASimulate	Simulate the ARIMA process.
	ARMAAcf	Estimates autocorrelation/autocovariance function for the ARMA model.
	ARMAForecast	Forecast time series by using ARMA(p,q) model.
	ARMAHannahFit	Hannah-Rissanen ARMA estimation.
	ARMAInnovationsFit	Innovations ARMA estimation.
	ARMAInnovationsFit	Innovations ARMA estimation.
	ARMAKappa	ARMA process covariances.
	ARMAKappa	Calculate necessary covariances for ARMA(p,q) process up to kappa(KappaSize,KappaSize)
	ARMALogLike	-2log likelihood.
	ARMAMLE	Estimate ARMA process AR and MA coefficients.
	ARMAPredictors	ARMA model one-step ahead predictors.
	ARMASimulate	Simulate the ARMA (p,q) process.
	ARYuleWalkerFit	Yule-Walker AR estimation.
	AutoCov	Autocovariance function.
	BoxCox	Box-Cox transformation.
	BoxCoxInv	Inverse Box-Cox transformation.
	BoxLjung	The box-Ljung statistics.
	CheckARMACoeffs	Check AR(MA) coeefficients.
	CheckARMACoeffs	This is function StatTimeSerAnalysis.CheckARMACoeffs.
	DoubleExpForecast	Double exponential forecast.
	DoubleExpForecast	First estimate Alpha and Gamma parameters by double smoothing and then use returned values to forecast up to T periods.
	DoubleExpSmooth	Double exponential smoothing.
	DoubleExpSmooth	In this case a fixed smoothing constants Alpha, Gamma are used in smoothing equations (no minimization is performed).
	DurbinLevinson	The Durbin-Levinson algorithm.
	DurbinWatson	Calculates the Durbin-Watson statistic
	Innovations	Uses the Innnovations algorithm to recursively calculate Theta[1,1]...Theta[n,n] coefficients (all coefficients).
	Innovations	The innovations algorithm.
	InvTransformParams	This is function StatTimeSerAnalysis.InvTransformParams.
	MovingAverage	Single moving average.
	PACF	Partial autocorelation function.
	PACF	Partial autocorrelation function.
	ShortenFilter	Memory-shortening filter.
	SingleExpForecast	Single exponential forecast.
	SingleExpForecast	rst estimate Alpha parameters by single smoothing and then use returned value to forecast up to T periods.
	SingleExpSmooth	Single exponential smoothing.
	SingleExpSmooth	In this case a fixed smoothing constant Alpha is used in smoothing equations (no minimization is performed).
	TimeSeriesIntInit	Setup initial values for integrating ARMA series.
	TransformParams	This is function StatTimeSerAnalysis.TransformParams.
	TripleExpForecast	Triple exponential forecast.
	TripleExpForecast	First estimate Alpha, Beta and Gamma parameters by triple exponential smoothing and then use returned values to forecast up to T periods.
	TripleExpSmooth	Triple exponential smoothing.
	TripleExpSmooth	In this case a fixed smoothing constants Alpha, Beta and Gamma are used in smoothing equations (no minimization is performed).

Structs, Records, Enums

	Name	Description
	TcfInitMethod	ARMA/ARIMA coefficients initial estimate method.

Links

Legend, Topics, Functions, Structs, Records, Enums

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