# Estimate a scalar ARMA processΒΆ

The objective here is to estimate an ARMA model from a scalar stationary time series using the Whittle estimator and a centered normal white noise.

The data can be a unique time series or several time series collected in a process sample.

If the user specifies the order , OpenTURNS fits a model to the data by estimating the coefficients and the variance of the white noise.

If the User specifies a range of orders , where and , We find the *best* model that fits to the data and estimates the corresponding coefficients.

We proceed as follows:

the object

*WhittleFactory*is created with either a specified order or a range . By default, the Welch estimator (object*Welch*) is used with its default parameters.for each order , the estimation of the parameters is done by maximizing the reduced equation of the Whittle likelihood function ([lik2]), thanks to the method

*build*of the object*WhittleFactory*. This method applies to a time series or a process sample. If the user wants to get the quantified criteria and*BIC*of the model , he has to specify it by giving a*Point*of size 0 (*Point()*) as input parameter of the method*build*.the output of the estimation is, in all the cases, one unique ARMA: the ARMA with the specified order or the optimal one with respect to the criterion.

in the case of a range , the user can get all the estimated models thanks to the method

*getHistory*of the object*WhittleFactory*. If the*build*has been parameterized by a*Point*of size 0, the user also has access to all the quantified criteria.

The synthetic data is generated using the following 1-d ARMA process:

with the noise defined as:

```
[5]:
```

```
from __future__ import print_function
import openturns as ot
import matplotlib.pyplot as plt
ot.RandomGenerator.SetSeed(0)
```

```
[6]:
```

```
# Create an arma process
tMin = 0.0
n = 1000
timeStep = 0.1
myTimeGrid = ot.RegularGrid(tMin, timeStep, n)
myWhiteNoise = ot.WhiteNoise(ot.Triangular(-1.0, 0.0, 1.0), myTimeGrid)
myARCoef = ot.ARMACoefficients([0.4, 0.3, 0.2, 0.1])
myMACoef = ot.ARMACoefficients([0.4, 0.3])
arma = ot.ARMA(myARCoef, myMACoef, myWhiteNoise)
tseries = ot.TimeSeries(arma.getRealization())
# Create a sample of N time series from the process
N = 100
sample = arma.getSample(N)
```

```
[7]:
```

```
# CASE 1 : we specify a (p,q) order
# Specify the order (p,q)
p = 4
q = 2
# Create the estimator
factory = ot.WhittleFactory(p, q)
print("Default spectral model factory = ", factory.getSpectralModelFactory())
# To set the spectral model factory
# For example, set WelchFactory as SpectralModelFactory
# with the Hanning filtering window
# The Welch estimator splits the time series in four blocs without overlap
myFilteringWindow = ot.Hanning()
mySpectralFactory = ot.WelchFactory(myFilteringWindow, 4, 0)
factory.setSpectralModelFactory(mySpectralFactory)
print("New spectral model factory = ", factory.getSpectralModelFactory())
# Estimate the ARMA model from a time series
# To get the quantified AICc, AIC and BIC criteria
arma42, criterion = factory.buildWithCriteria(tseries)
AICc, AIC, BIC = criterion[0:3]
print('AICc=', AICc, 'AIC=', AIC, 'BIC=', BIC)
arma42
```

```
Default spectral model factory = class=WelchFactory window = class=FilteringWindows implementation=class=Hamming blockNumber = 1 overlap = 0
New spectral model factory = class=WelchFactory window = class=FilteringWindows implementation=class=Hanning blockNumber = 4 overlap = 0
AICc= 771.8917262722518 AIC= 770.9344613149868 BIC= 824.530853637219
```

```
[7]:
```

ARMA(X_{0,t} - 0.214424 X_{0,t-1} + 0.432622 X_{0,t-2} + 0.203859 X_{0,t-3} + 0.0512422 X_{0,t-4} = E_{0,t} - 0.194383 E_{0,t-1} + 0.461067 E_{0,t-2}, E_t ~ Normal(mu = 0, sigma = 0.406619))

```
[8]:
```

```
# CASE 2 : we specify a range of (p,q) orders
###################################
# Range for p
pIndices = [1, 2, 4]
# Range for q = [4,5,6]
qIndices = [4, 5, 6]
# Build a Whittle factory with default SpectralModelFactory (WelchFactory)
# this time using ranges of order p and q
factory_range = ot.WhittleFactory(pIndices, qIndices)
# Estimate the arma model from a process sample
arma_range, criterion = factory_range.buildWithCriteria(sample)
AICc, AIC, BIC = criterion[0:3]
print('AICc=', AICc, 'AIC=', AIC, 'BIC=', BIC)
arma_range
```

```
AICc= 4443.4456045627585 AIC= 4443.217962286336 BIC= 4516.222475664246
```

```
[8]:
```

ARMA(X_{0,t} + 0.382771 X_{0,t-1} + 0.185752 X_{0,t-2} = E_{0,t} + 0.385312 E_{0,t-1} + 0.192682 E_{0,t-2} - 0.191497 E_{0,t-3} - 0.102842 E_{0,t-4}, E_t ~ Normal(mu = 0, sigma = 0.409595))

```
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```

```
# Results exploitation
# Get the white noise of the (best) estimated arma
arma_range.getWhiteNoise()
```

```
[9]:
```

WhiteNoise(Normal(mu = 0, sigma = 0.409595))