May 22, 2016 / by James Balamuta / In computing /

### Process to Haar Wavelet Variance Formulae

The following equations are derivations used within the package as they relate to the Haar Wavelet Variance (WV) theoretical quantities. The initial WV formula, , are used to calculate process to wavelet variance. The later are used within the asymptotic model selection calculations.

The initial equations, marked by , come from *Allan variance of time series models for measurement data* by Nien Fan Zhang published in Metrologia and *Analysis and Modeling of Inertial Sensors Using Allan Variance* by El-Sheimy, et. al. in IEEE Transactions on Instrumentation and Measurement. That is, these equations are derived using the Allan Variance (AV). The relationship between the Allan Variance to the Wavelet Variance is . Note, the used in the Allan Variance is equivalent to .

The derivations below were done using Mathematica. The derivation file is available at: http://smac-group.com/assets/supporting_docs/haar_analytical_derivatives_complete.nb

If you notice one of the derivations as being incorrected, please let us know via an issue at https://github.com/smac-group/gmwm/issues.

##### White Noise

##### Random Walk

##### Drift Process

##### Quantization Noise (QN)

##### AR 1 Process

**Derivatives w.r.t. **

**Derivatives w.r.t. **

**Derivative w.r.t both and **

Here we opted to take the derivative w.r.t to first and then . The order of derivatives do not matter due to Clairaut’s Theorem.

##### MA 1 Process

*>* **NOTE** For the MA(1) process listed in Zhang on Page 552, there is a sign error between equations (21) and (22). This has been corrected here.

**Derivatives w.r.t **

**Derivatives w.r.t. **

**Derivative w.r.t both and **

##### ARMA(1,1)

**NOTE** For the ARMA(1,1) process listed in Zhang on Page 553, he references Time Series Analysis:
Forecasting and Control by Box G E P and Jenkins G M 1976 that contains an error when describing both the process variance and autocorrelation function (ACF).

In this case, the ARMA(1,1) process variance, , and first autocovariance,, is given by:

And the ARMA(1,1)’s autocorrelation function (ACF) is given by:

for .

With this in mind, we rederive the Allan Variance for an ARMA(1,1) using Equation 11 on page 551.

##### ARMA(1,1) Derivation

We begin by stating Equation 11 on page 551:

**Aside:** To continue, we need to solve the series formulation using the recursive properties of ARMA(1,1)’s ACF.

**Returning:** We substitute in to the first equation to obtain the Allan Variance for the ARMA(1,1) process.

##### ARMA(1,1) Process

**Derivative w.r.t **:

**Derivative w.r.t :**

**Derivative w.r.t. :**

**Derivative w.r.t. and :**

**Derivative w.r.t. and :**

**Derivative w.r.t. and :**