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| 63 | <h1><a class="anchor" name="tut_arx">Theory of ARX model estimation </a></h1><p> |
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| 64 | The <code>ARX</code> (AutoregRessive with eXogeneous input) model is defined as follows: <p class="formulaDsp"> |
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| 65 | <img class="formulaDsp" alt="\[ y_t = \theta' \psi_t + \rho e_t \]" src="form_107.png"> |
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| 66 | <p> |
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| 67 | where <img class="formulaInl" alt="$y_t$" src="form_42.png"> is the system output, <img class="formulaInl" alt="$[\theta,\rho]$" src="form_108.png"> is vector of unknown parameters, <img class="formulaInl" alt="$\psi_t$" src="form_109.png"> is an vector of data-dependent regressors, and noise <img class="formulaInl" alt="$e_t$" src="form_6.png"> is assumed to be Normal distributed <img class="formulaInl" alt="$\mathcal{N}(0,1)$" src="form_110.png">.<p> |
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| 68 | Special cases include: <ul> |
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| 69 | <li>estimation of unknown mean and variance of a Gaussian density from independent samples.</li> |
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| 70 | </ul> |
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| 71 | <h2><a class="anchor" name="off"> |
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| 72 | Off-line estimation:</a></h2> |
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| 73 | This particular model belongs to the exponential family, hence it has conjugate distribution (i.e. both prior and posterior) of the Gauss-inverse-Wishart form. See [ref]<p> |
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| 74 | Estimation of this family can be achieved by accumulation of sufficient statistics. The sufficient statistics Gauss-inverse-Wishart density is composed of: <dl> |
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| 75 | <dt>Information matrix </dt> |
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| 76 | <dd>which is a sum of outer products <p class="formulaDsp"> |
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| 77 | <img class="formulaDsp" alt="\[ V_t = \sum_{i=0}^{n} \left[\begin{array}{c}y_{t}\\ \psi_{t}\end{array}\right] \begin{array}{c} [y_{t}',\,\psi_{t}']\\ \\\end{array} \]" src="form_111.png"> |
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| 78 | <p> |
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| 79 | </dd> |
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| 80 | <dt>"Degree of freedom" </dt> |
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| 81 | <dd>which is an accumulator of number of data records <p class="formulaDsp"> |
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| 82 | <img class="formulaDsp" alt="\[ \nu_t = \sum_{i=0}^{n} 1 \]" src="form_112.png"> |
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| 83 | <p> |
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| 84 | </dd> |
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| 85 | </dl> |
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| 86 | <h2><a class="anchor" name="on"> |
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| 87 | On-line estimation</a></h2> |
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| 88 | For online estimation with stationary parameters can be easily achieved by collecting the sufficient statistics described above recursively.<p> |
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| 89 | Extension to non-stationaly parameters, <img class="formulaInl" alt="$ \theta_t , r_t $" src="form_113.png"> can be achieved by operation called forgetting. This is an approximation of Bayesian filtering see [Kulhavy]. The resulting algorithm is defined by manipulation of sufficient statistics: <dl> |
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| 90 | <dt>Information matrix </dt> |
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| 91 | <dd>which is a sum of outer products <p class="formulaDsp"> |
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| 92 | <img class="formulaDsp" alt="\[ V_t = \phi V_{t-1} + \left[\begin{array}{c}y_{t}\\ \psi_{t}\end{array}\right] \begin{array}{c} [y_{t}',\,\psi_{t}']\\ \\\end{array} +(1-\phi) V_0 \]" src="form_114.png"> |
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| 93 | <p> |
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| 94 | </dd> |
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| 95 | <dt>"Degree of freedom" </dt> |
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| 96 | <dd>which is an accumulator of number of data records <p class="formulaDsp"> |
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| 97 | <img class="formulaDsp" alt="\[ \nu_t = \phi \nu_{t-1} + 1 + (1-\phi) \nu_0 \]" src="form_115.png"> |
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| 98 | <p> |
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| 99 | </dd> |
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| 100 | </dl> |
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| 101 | where <img class="formulaInl" alt="$ \phi $" src="form_116.png"> is the forgetting factor, typically <img class="formulaInl" alt="$ \phi \in [0,1]$" src="form_117.png"> roughly corresponding to the effective length of the exponential window by relation:<p class="formulaDsp"> |
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| 102 | <img class="formulaDsp" alt="\[ \mathrm{win_length} = \frac{1}{1-\phi}\]" src="form_118.png"> |
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| 103 | <p> |
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| 104 | Hence, <img class="formulaInl" alt="$ \phi=0.9 $" src="form_119.png"> corresponds to estimation on exponential window of effective length 10 samples.<p> |
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| 105 | Statistics <img class="formulaInl" alt="$ V_0 , \nu_0 $" src="form_120.png"> are called alternative statistics, their role is to stabilize estimation. It is easy to show that for zero data, the statistics <img class="formulaInl" alt="$ V_t , \nu_t $" src="form_121.png"> converge to the alternative statistics.<h2><a class="anchor" name="str"> |
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| 106 | Structure estimation</a></h2> |
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| 107 | For this model, structure estimation is a form of model selection procedure. Specifically, we compare hypotheses that the data were generated by the full model with hypotheses that some regressors in vector <img class="formulaInl" alt="$\psi$" src="form_15.png"> are redundant. The number of possible hypotheses is then the number of all possible combinations of all regressors.<p> |
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| 108 | However, due to property known as nesting in exponential family, these hypotheses can be tested using only the posterior statistics. (This property does no hold for forgetting <img class="formulaInl" alt="$ \phi<1 $" src="form_122.png">). Hence, for low dimensional problems, this can be done by a tree search (method <a class="el" href="classbdm_1_1ARX.html#16b02ae03316751664c22d59d90c1e34" title="Brute force structure estimation.">bdm::ARX::structure_est()</a>). Or more sophisticated algorithm [ref Ludvik]<h2><a class="anchor" name="soft"> |
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| 109 | Software Image</a></h2> |
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| 110 | Estimation of the ARX model is implemented in class <a class="el" href="classbdm_1_1ARX.html" title="Linear Autoregressive model with Gaussian noise.">bdm::ARX</a>. <ul> |
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| 111 | <li>models from exponential family share some properties, these are encoded in class <a class="el" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">bdm::BMEF</a> which is the parent of ARX </li> |
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| 112 | <li>one of the parameters of <a class="el" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">bdm::BMEF</a> is the forgetting factor which is stored in attribute <code>frg</code>, </li> |
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| 113 | <li>posterior density is stored inside the estimator in the form of <a class="el" href="classbdm_1_1egiw.html" title="Gauss-inverse-Wishart density stored in LD form.">bdm::egiw</a> </li> |
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| 114 | <li>references to statistics of the internal <code>egiw</code> class, i.e. attributes <code>V</code> and <code>nu</code> are established for convenience.</li> |
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| 115 | </ul> |
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| 116 | <h2><a class="anchor" name="try"> |
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| 117 | How to try</a></h2> |
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| 118 | The best way to experiment with this object is to run matlab script <code>arx_test.m</code> located in directory <code></code>./library/tutorial. See <a class="el" href="arx_ui.html">Running experiment <code>estimator</code> with ARX data fields</a> for detailed description.<p> |
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| 119 | <ul> |
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| 120 | <li>In default setup, the parameters converge to the true values as expected. </li> |
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| 121 | <li>Try changing the forgetting factor, field <code>estimator.frg</code>, to values <1. You should see increased lower and upper bounds on the estimates. </li> |
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| 122 | <li>Try different set of parameters, filed <code>system.theta</code>, you should note that poles close to zero are harder to identify. </li> |
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| 123 | </ul> |
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