#

# STATISTICAL TIME SERIES ANALYSIS (STSA) module for O-Matrix.

#

# Written by Dr. Dimitrios D. Thomakos

# University of Peloponesse

# e-mail: thomakos@uop.gr

#

# STSA Version 2.0

#

# Example file #17

#

# In this file the following functions are demonstrated:

#

# acvf_spectrum

# ar_order

# ar_spectrum

# arma_simulate

# plot_spectrum

# spectrum

#

 

# Initialize

clear

ginit

clc

 

# Simulate an autoregressive model with an deterministic oscillating component

mu = 0.

phi = { 0.6, 0.2 }

theta = 0.

sigma = 0.4

N = 400.

u = arma_simulate(mu,phi,theta,sigma,N)

# Add the deterministic component here

A = 0.7

B = -1.

omega = 2*pi*(12./N)

t = seq(int(N))

r = A%sin(omega*t+B)

# Put them together

x = r + u

 

# Plot the deterministic component and the series

gcolor({"black","green"})

gxaxis("linear",0,N,10)

gplot(r)

gplot(x)

 

# Compute the power spectrum of the series using three methods;

# plot the output of the first method using the plot_spectrum function

# and the output of the other two methods together in a new window

#

# First method: smooth the periodogram using a sine taper

K = 15.

[f,S1] = spectrum(x,1,K)

plot_spectrum(f,S1,"linear",1,"X - first method")

# Second method: smooth the autocovariances

M = 100.

[f,S2] = acvf_spectrum(x,M)

# Third method: autoregressive approximation

# first we select the order

[aic,bic] = ar_order(x,12,1,1)

print "Selected AICc order = ", aic(1,1)

print "Selected BIC  order = ", bic(1,1)

print

# then estimate using the AICc order

[f,S3] = ar_spectrum(x,aic(1,1),1)

# Now, plot the second and thid method spectra

gaddwin("Spectra second (blue) and third (red) methods")

gxaxis("linear",0,0.5,10)

gxtitle("Frequency")

gytitle("Power")

gcolor({"blue","red"})

gplot(f,[S2,S3])

 

print "You can manually adjust the following parameters in experimenting with the"

print "different methods of spectral estimation:"

print

print "sigma : controls the noise of the series"

print "K : number of sine tapers used in the first method"

print "M : number of autocovariances used in the second method"

print

print "You can also try out different orders for the autoregressive spectral"

print "estimation without having to estimate the orders using the AICc and BIC criteria"

print