OPEN-SOURCE SCRIPT
Weighted Burg AR Spectral Estimate Extrapolation of Price [Loxx]
Güncellendi
Weighted Burg AR Spectral Estimate Extrapolation of Price [Loxx] is an indicator that uses an autoregressive spectral estimation called the Weighted Burg Algorithm. This method is commonly used in speech modeling and speech prediction engines. This method also includes Levinson–Durbin algorithm. As was already discussed previously in the following indicator:
Levinson-Durbin Autocorrelation Extrapolation of Price [Loxx]
![Levinson-Durbin Autocorrelation Extrapolation of Price [Loxx]](https://s3.tradingview.com/m/mvUdLxSg_mid.png)
What is Levinson recursion or Levinson–Durbin recursion?
In many applications, the duration of an uninterrupted measurement of a time series is limited. However, it is often possible to obtain several separate segments of data. The estimation of an autoregressive model from this type of data is discussed. A straightforward approach is to take the average of models estimated from each segment separately. In this way, the variance of the estimated parameters is reduced. However, averaging does not reduce the bias in the estimate. With the Burg algorithm for segments, both the variance and the bias in the estimated parameters are reduced by fitting a single model to all segments simultaneously. As a result, the model estimated with the Burg algorithm for segments is more accurate than models obtained with averaging. The new weighted Burg algorithm for segments allows combining segments of different amplitudes.
The Burg algorithm estimates the AR parameters by determining reflection coefficients that minimize the sum of for-ward and backward residuals. The extension of the algorithm to segments is that the reflection coefficients are estimated by minimizing the sum of forward and backward residuals of all segments taken together. This means a single model is fitted to all segments in one time. This concept is also used for prediction error methods in system identification, where the input to the system is known, like in ARX modeling
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
BurgWin - weighing function index, rectangular, hamming, or parabolic
Things to know
Included
Further reading
Performance of the weighted burg methods of ar spectral estimation for pitch-synchronous analysis of voiced speech
The Burg algorithm for segments
Techniques for the Enhancement of Linear Predictive Speech Coding in Adverse Conditions
Related Indicators
Levinson-Durbin Autocorrelation Extrapolation of Price [Loxx]
What is Levinson recursion or Levinson–Durbin recursion?
In many applications, the duration of an uninterrupted measurement of a time series is limited. However, it is often possible to obtain several separate segments of data. The estimation of an autoregressive model from this type of data is discussed. A straightforward approach is to take the average of models estimated from each segment separately. In this way, the variance of the estimated parameters is reduced. However, averaging does not reduce the bias in the estimate. With the Burg algorithm for segments, both the variance and the bias in the estimated parameters are reduced by fitting a single model to all segments simultaneously. As a result, the model estimated with the Burg algorithm for segments is more accurate than models obtained with averaging. The new weighted Burg algorithm for segments allows combining segments of different amplitudes.
The Burg algorithm estimates the AR parameters by determining reflection coefficients that minimize the sum of for-ward and backward residuals. The extension of the algorithm to segments is that the reflection coefficients are estimated by minimizing the sum of forward and backward residuals of all segments taken together. This means a single model is fitted to all segments in one time. This concept is also used for prediction error methods in system identification, where the input to the system is known, like in ARX modeling
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
BurgWin - weighing function index, rectangular, hamming, or parabolic
Things to know
- Normally, a simple moving average is calculated 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
Performance of the weighted burg methods of ar spectral estimation for pitch-synchronous analysis of voiced speech
The Burg algorithm for segments
Techniques for the Enhancement of Linear Predictive Speech Coding in Adverse Conditions
Related Indicators
Sürüm Notları
Small error fix.Sürüm Notları
Small fix in the description. The emboldened title "What is Levinson recursion or Levinson–Durbin recursion?" should be "What is the Weighted Burg Autoregressive Spectral Estimate Extrapolation of Price?"Sürüm Notları
Fixed calculation error.Sürüm Notları
Changed plot to line and cleaned up coordinate drawing functions.Sürüm Notları
Coordinate cleanup.Sürüm Notları
Updated drawing functions. Sürüm Notları
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. Sürüm Notları
Fixed error and removed smoothing, it wasn't having the desired effect.Sürüm Notları
Removed unused inputs.Sürüm Notları
Updated lines calculation Açık kaynak kodlu komut dosyası
Gerçek TradingView ruhuna uygun olarak, bu komut dosyasının yazarı komut dosyasını açık kaynak olarak yayınlamıştır, böylece yatırımcılar betiği anlayabilir ve doğrulayabilir. Yazar çok yaşa! Ücretsiz olarak kullanabilirsiniz, ancak bu kodun yayında yeniden kullanımı Ev kurallarına tabidir. Bir grafikte kullanmak için favorilere ekleyebilirsiniz.
Bu komut dosyasını bir grafikte kullanmak ister misiniz?
Public Telegram Group, t.me/algxtrading_public
VIP Membership Info: patreon.com/algxtrading/membership
VIP Membership Info: patreon.com/algxtrading/membership
Feragatname
Bilgiler ve yayınlar, TradingView tarafından sağlanan veya onaylanan finansal, yatırım, işlem veya diğer türden tavsiye veya tavsiyeler anlamına gelmez ve teşkil etmez. Kullanım Şartları'nda daha fazlasını okuyun.