Levinson-Durbin Autocorrelation Extrapolation of Price [Loxx]

What is Levinson recursion or Levinson–Durbin recursion?
Is a linear algebra prediction analysis that is performed once per bar using the autocorrelation method with a within a specified asymmetric window. The autocorrelation coefficients of the window are computed and converted to LP coefficients using the Levinson algorithm. The LP coefficients are then transformed to line spectrum pairs for quantization and interpolation. The interpolated quantized and unquantized filters are converted back to the LP filter coefficients to construct the synthesis and weighting filters for each bar.
Data inputs
Source Settings: -Loxx's Expanded Source Types. You typically use "open" since open has already closed on the current active bar
LastBar - bar where to start the prediction
PastBars - how many bars back to model
LPOrder - order of linear prediction model; 0 to 1
FutBars - how many bars you want to forward predict
Things to know
- Normally, a simple moving average is caculated on source data. I've expanded this to 38 different averaging methods using Loxx's Moving Avreages.
- This indicator repaints
Included
- Bar color muting
Further reading
Implementing the Levinson-Durbin Algorithm on the StarCore™ SC140/SC1400 Cores
LevinsonDurbin_G729 Algorithm, Calculates LP coefficients from the autocorrelation coefficients. Intel® Integrated Performance Primitives for Intel® Architecture Reference Manual
// Original Levinson-Durbin algorithm used to implement Levinson recursion
// where a - coefficients of the model, p - order of the model.
// Here we need to find the autoregressive coefficients by solving directly
// our set of equations with n=2*p by the Levinson-Durbin method. Such method
// of prediction is called Prony Method; however, its disadvantage is the
// instability during the prediction of the future values of the series. That's
// why this method has not been included and instead we use a modified
// Levinson Recursion to calculate the prediction coefficients.
// I've included the origina method so one can compare the differences. You'll
// notice that both methods are very similar but the modified version gives the
// desired results. The difference is that the modified version calculates the
// coefficients a[] by decreasing the mean-root-square error on the training
// last n-p bars
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Penafian
Skrip sumber terbuka
Dalam semangat sebenar TradingView, pencipta skrip ini telah menjadikannya sumber terbuka supaya pedagang dapat menilai dan mengesahkan kefungsiannya. Terima kasih kepada penulis! Walaupun anda boleh menggunakannya secara percuma, ingat bahawa menerbitkan semula kod ini adalah tertakluk kepada Peraturan Dalaman kami.
Untuk akses pantas pada carta, tambah skrip ini kepada kegemaran anda — ketahui lebih lanjut di sini.
VIP Membership Info: patreon.com/algxtrading/membership