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已更新 Modified Covariance Autoregressive Estimator of Price [Loxx]

What is the Modified Covariance AR Estimator?
The Modified Covariance AR Estimator uses the modified covariance method to fit an autoregressive (AR) model to the input data. This method minimizes the forward and backward prediction errors in the least squares sense. The input is a frame of consecutive time samples, which is assumed to be the output of an AR system driven by white noise. The block computes the normalized estimate of the AR system parameters, A(z), independently for each successive input.
Characteristics of Modified Covariance AR Estimator
Purpose
This indicator calculates a prediction of price. This will NOT work on all tickers. To see whether this works on a ticker for the settings you have chosen, you must check the label message on the lower right of the chart. The label will show either a pass or fail. If it passes, then it's green, if it fails, it's red. The reason for this is because the Modified Covariance method produce unstable models
H(z)= G / A(z) = G / (1+. a(2)z −1 +…+a(p+1)z)
You specify the order, "ip", of the all-pole model in the Estimation order parameter. To guarantee a valid output, you must set the Estimation order parameter to be less than or equal to two thirds the input vector length.
The output port labeled "a" outputs the normalized estimate of the AR model coefficients in descending powers of z.
The implementation of the Modified Covariance AR Estimator in this indicator is the fast algorithm for the solution of the modified covariance least squares normal equations.
Inputs
x - Array of complex data samples X(1) through X(N)
ip - Order of linear prediction model (integer)
Notable local variables
v - Real linear prediction variance at order IP
Outputs
a - Array of complex linear prediction coefficients
stop - value at time of exit, with error message
false - for normal exit (no numerical ill-conditioning)
true - if v is not a positive value
true - if delta and gamma do not lie in the range 0 to 1
true - if v is not a positive value
true - if delta and gamma do not lie in the range 0 to 1
errormessage - an error message based on "stop" parameter; this message will be displayed in the lower righthand corner of the chart. If you see a green "passed" then the analysis is valid, otherwise the test failed.
Indicator inputs
LastBar = bars backward from current bar to test estimate reliability
PastBars = how many bars are we going to analyze
LPOrder = Order of Linear Prediction, and for Modified Covariance AR method, this must be less than or equal to 2/3 the input frame size, so this number has a max value of 0.67
FutBars = how many bars you'd like to show in the future. This algorithm will either accept or reject your value input here and then project forward
Further reading
Spectrum Analysis-A Modern Perspective 1380 PROCEEDINGS OF THE IEEE, VOL. 69, NO. 11, NOVEMBER 1981
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Levinson-Durbin Autocorrelation Extrapolation of Price [Loxx]
![Levinson-Durbin Autocorrelation Extrapolation of Price [Loxx]](https://s3.tradingview.com/m/mvUdLxSg_mid.png)
Weighted Burg AR Spectral Estimate Extrapolation of Price [Loxx]
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Helme-Nikias Weighted Burg AR-SE Extra. of Price [Loxx]
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Itakura-Saito Autoregressive Extrapolation of Price [Loxx]
![Itakura-Saito Autoregressive Extrapolation of Price [Loxx]](https://s3.tradingview.com/b/BKC6dFUY_mid.png)
Modified Covariance Autoregressive Estimator of Price [Loxx]
![Fourier Extrapolator of Price w/ Projection Forecast [Loxx]](https://s3.tradingview.com/u/u0r2gpti_mid.png)
The Modified Covariance AR Estimator uses the modified covariance method to fit an autoregressive (AR) model to the input data. This method minimizes the forward and backward prediction errors in the least squares sense. The input is a frame of consecutive time samples, which is assumed to be the output of an AR system driven by white noise. The block computes the normalized estimate of the AR system parameters, A(z), independently for each successive input.
Characteristics of Modified Covariance AR Estimator
- Minimizes the forward prediction error in the least squares sense
- Minimizes the forward and backward prediction errors in the least squares sense
- High resolution for short data records
- Able to extract frequencies from data consisting of p or more pure sinusoids
- Does not suffer spectral line-splitting
- May produce unstable models
- Peak locations slightly dependent on initial phase
- Minor frequency bias for estimates of sinusoids in noise
- Order must be less than or equal to 2/3 the input frame size
Purpose
This indicator calculates a prediction of price. This will NOT work on all tickers. To see whether this works on a ticker for the settings you have chosen, you must check the label message on the lower right of the chart. The label will show either a pass or fail. If it passes, then it's green, if it fails, it's red. The reason for this is because the Modified Covariance method produce unstable models
H(z)= G / A(z) = G / (1+. a(2)z −1 +…+a(p+1)z)
You specify the order, "ip", of the all-pole model in the Estimation order parameter. To guarantee a valid output, you must set the Estimation order parameter to be less than or equal to two thirds the input vector length.
The output port labeled "a" outputs the normalized estimate of the AR model coefficients in descending powers of z.
The implementation of the Modified Covariance AR Estimator in this indicator is the fast algorithm for the solution of the modified covariance least squares normal equations.
Inputs
x - Array of complex data samples X(1) through X(N)
ip - Order of linear prediction model (integer)
Notable local variables
v - Real linear prediction variance at order IP
Outputs
a - Array of complex linear prediction coefficients
stop - value at time of exit, with error message
false - for normal exit (no numerical ill-conditioning)
true - if v is not a positive value
true - if delta and gamma do not lie in the range 0 to 1
true - if v is not a positive value
true - if delta and gamma do not lie in the range 0 to 1
errormessage - an error message based on "stop" parameter; this message will be displayed in the lower righthand corner of the chart. If you see a green "passed" then the analysis is valid, otherwise the test failed.
Indicator inputs
LastBar = bars backward from current bar to test estimate reliability
PastBars = how many bars are we going to analyze
LPOrder = Order of Linear Prediction, and for Modified Covariance AR method, this must be less than or equal to 2/3 the input frame size, so this number has a max value of 0.67
FutBars = how many bars you'd like to show in the future. This algorithm will either accept or reject your value input here and then project forward
Further reading
Spectrum Analysis-A Modern Perspective 1380 PROCEEDINGS OF THE IEEE, VOL. 69, NO. 11, NOVEMBER 1981
Related indicators
Levinson-Durbin Autocorrelation Extrapolation of Price [Loxx]
![Levinson-Durbin Autocorrelation Extrapolation of Price [Loxx]](https://s3.tradingview.com/m/mvUdLxSg_mid.png)
Weighted Burg AR Spectral Estimate Extrapolation of Price [Loxx]
![Weighted Burg AR Spectral Estimate Extrapolation of Price [Loxx]](https://s3.tradingview.com/g/G3VyvCoH_mid.png)
Helme-Nikias Weighted Burg AR-SE Extra. of Price [Loxx]
![Helme-Nikias Weighted Burg AR-SE Extra. of Price [Loxx]](https://s3.tradingview.com/8/8YbmmPCn_mid.png)
Itakura-Saito Autoregressive Extrapolation of Price [Loxx]
![Itakura-Saito Autoregressive Extrapolation of Price [Loxx]](https://s3.tradingview.com/b/BKC6dFUY_mid.png)
Modified Covariance Autoregressive Estimator of Price [Loxx]
![Fourier Extrapolator of Price w/ Projection Forecast [Loxx]](https://s3.tradingview.com/u/u0r2gpti_mid.png)
版本注释
Updated lines calculation. 开源脚本
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VIP Membership Info: patreon.com/algxtrading/membership
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这些信息和出版物并不意味着也不构成TradingView提供或认可的金融、投资、交易或其它类型的建议或背书。请在使用条款阅读更多信息。
开源脚本
本着TradingView的真正精神,此脚本的创建者将其开源,以便交易者可以查看和验证其功能。向作者致敬!虽然您可以免费使用它,但请记住,重新发布代码必须遵守我们的网站规则。
Public Telegram Group, t.me/algxtrading_public
VIP Membership Info: patreon.com/algxtrading/membership
VIP Membership Info: patreon.com/algxtrading/membership
免责声明
这些信息和出版物并不意味着也不构成TradingView提供或认可的金融、投资、交易或其它类型的建议或背书。请在使用条款阅读更多信息。