Levinson-Durbin Autocorrelation Extrapolation of Price [Loxx] is an indicator that uses the Levinson recursion or Levinson–Durbin recursion algorithm to predict price moves. This method is commonly used in speech modeling and prediction engines.
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.
Changed plot to line and cleaned up coordinate drawing functions.
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Coordinate cleanup.
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Updated drawing functions.
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Increased lookback range to max of 2000 bars. Future bar draws are limited to math.min(array output calcs, FutBars) settings. All settings work now.. You'll need to adjust the settings if it shows an error that ran out of processing time or took too long to execute.
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Fixed error and removed smoothing, it wasn't having the desired effect.
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Removed unused inputs.
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Updated lines calculation
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I added the following comment to the code so folks can better understand what is going on here. You can see the original Levinson-Durbin Recursion algorithm in the commented out note.
// 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
Hi and thank you for a nice script. I have two questions, 1. Do you find better prediction results when using heikin ashi with this script? 2. Have you found any correlation with better results and changing the number of look back bars depending on the time period? For instance using larger or fewer number of look back bars when using 1-5 min chart than for instance 4H chart.
reees
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Cool concept! Naturally seems to work well when markets are ranging. I wonder if you could bake a bias into the algorithm for trending markets? Appreciate the open source script, good stuff in there.