```
DEFUN_DLD (linearpredict, args, , "Help String")
{
octave_value retval;
ColumnVector a = args(0).column_vector_value (); // The input price
ColumnVector b(a); // This will be the output column vector returned to Octave by "retval"
const int n = args(1).int_value();
int lngth = 10;
double g[30];
int zz;
for (zz = 0; zz < 30; zz++)
g[zz] = 0.0;
double sigPredict[30];
for (zz = 0; zz < 30; zz++)
sigPredict[zz] = 0.0;
double sigPower = 0.0;
double mu = 0.0;
double xBar = 0.0;
int jj = 0;
for (octave_idx_type ii (10); ii < a.length="" 10="" a="" average="" factor="" for="" href="https://draft.blogger.com/null" if="" ii="" jj="" lngth="" loop="" normalization="" ompute="" onvergence="" power="" sigpower="" start="" the=""> 0)
mu = 0.25 / (sigPower * 10);
//Compute signal estimate
xBar = 0;
for (jj = 1; jj <= lngth; jj++)
xBar = xBar + a(ii - jj) * g[jj];
//Compute gain coefficients
for (jj = 1; jj <= lngth; jj++)
g[jj] = g[jj] + (mu * (a(ii) - xBar) * a(ii - jj));
//Compute signal prediction waveform
for (jj = 0; jj <= lngth; jj++)
sigPredict[jj] = a(ii - (10 - jj));
//Extend signal prediction into the future
int kk = 0;
for (jj = lngth + 1; jj <= lngth + 5; jj++)
{
sigPredict[jj] = 0;
for (kk = 1; kk <= lngth; kk++)
sigPredict[jj] = sigPredict[jj] + sigPredict[jj - kk] * g[kk];
}
b(ii) = sigPredict[lngth + n];
}
retval = b; // Assign the output column vector to the return value
return retval; // Return the output to Octave
}
```

which is very similar in concept to Burg's method. I think some application of these methods show more promise than concentrating on my naive implementation of Runge-Kutta.
## Tuesday, 13 October 2015

### Giving up on Runge-Kutta Methods (for now?)

Over the last few weeks I have been looking at using Runge-Kutta methods for the creation of features, but I have decided to give up on this for now simply because I think I have found a better way to accomplish what I want. I was alerted to this possible approach by this post over at http://dsp.stackexchange.com/ and following up on this I remembered that a few years ago I coded up John Ehler's Linear Prediction Filter (the white paper for which might still be available here) and my crudely transcribed code given below:

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