Autoregressive process modeling tools in header-only C++
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Autoregressive modeling tools in header-only C++


This package contains a precision-agnostic, header-only, C++ implementation of Burg's recursive method for estimating autoregressive model parameters. Many usability-related extensions, in particular Octave- and Python-friendly functions, have been added to permit simply obtaining autocorrelation information from the resulting estimated model.

The implementation permits extracting a sequence of AR(p) models for p from one up to some maximum order:

Estimating at most an AR(7) model using 10 samples

AR      RMS/N      Gain Filter Coefficients
--      -----      ---- -------------------
 0   5.91e+00  1.00e+00 [ 1  ]
 1   1.71e-03  3.45e+03 [ 1   -0.9999 ]
 2   2.01e-05  2.94e+05 [ 1    -1.994   0.9941 ]
 3   2.55e-08  2.32e+08 [ 1    -2.987    2.987  -0.9994 ]
 4   1.01e-09  5.83e+09 [ 1    -3.967    5.913   -3.927   0.9799 ]
 5   2.61e-11  2.26e+11 [ 1    -4.934    9.789   -9.763    4.895   -0.987 ]
 6   3.42e-12  1.73e+12 [ 1    -5.854    14.35   -18.86    14.02   -5.586   0.9322 ]
 7   3.65e-13  1.62e+13 [ 1    -6.735    19.63   -32.12    31.85   -19.15    6.465  -0.9452 ]

AIC  selects model order 7 as best
AICC selects model order 6 as best
CIC  selects model order 7 as best

A variety of finite sample model selection criteria are implemented following [Broersen2000]. In particular, the

  • generalized information criterion (GIC),
  • Akaike information criterion (AIC),
  • consistent criterion BIC,
  • minimally consistent criterion (MCC),
  • asymptotically-corrected Akaike information criterion (AICC),
  • finite information criterion (FIC),
  • finite sample information criterion (FSIC), and
  • combined information criterion (CIC)

are all implemented. An included sample program called arsel uses CIC to select the best model order given data from standard input. It also estimates the effective sample size and corresponding variance using ideas from [Trenberth1984], [Thiebaux1984], and [vonStorch2001]. For example, arsel --subtract-mean < rhoe.dat reproduces results from ARMASA [Broersen2002] on a turbulence signal:

# absrho    true
# criterion CIC
# eff_N     28.18777014115533
# eff_var   3.6732508957963943e-05
# gain      4249.4040527950729
# maxorder  512
# minorder  0
# mu        0.20955287956200269
# mu_sigma  0.0011415499935005066
# N         1753
# AR(p)     6
# sigma2eps 8.3374920647988362e-09
# sigma2x   3.5429372570302933e-05
# submean   true
# T0        62.190091348891279
# window_T0 1

Also included is a Toeplitz linear equation solver for a single right hand side using O(3m^2) operations. This solver is useful for investigating the correctness and numerical stability of estimated process parameters and autocorrelation information. The algorithm is [Zohar1974]'s improvement of [Trench1967]'s work. See [Bunch1985] for a discussion of the stability of Trench-like algorithms and for faster, albeit much more complicated, variants.

Topmost row of Toeplitz matrix is:
        1 2 3 5 7 11 13 17
Leftmost column of Toeplitz matrix is:
        1 2 4 8 16 32 64 128
Right hand side data is:
        1 2 3 4 5 6 7 8
Expected solution is:
        -0.62963 0.148148 3.55556 -1.66667 0 -2 -1 2
Solution computed by zohar_linear_solve is:
        -0.62963 0.148148 3.55556 -1.66667 7.10543e-15 -2 -1 2
Term-by-term errors are:
        5.55112e-16 1.04361e-14 -2.70894e-14 9.99201e-15 -7.10543e-15 4.44089e-15 1.26565e-14 -9.32587e-15
Sum of the absolute errors is:

The automated model selection procedure exposed by arsel.cpp has been extensively tested against simulated data from the Lorenz attractor as implemented in lorenz.cpp. Please see [Oliver2014] for full details.


Try make followed by make check. On Linux, try make stress to examine the implementation's performance when piping in plain text data. Octave and/or Python functionality also will be built in-place when possible.
The standalone header implementing all algorithms. Complete API documentation is available at
Given data on standard input, use Burg's method to compute a hierarchy of candidate models and select the best one using CIC. Try arsel --help to see available options. This is perhaps the most useful standalone utility.
arsel-octfile.cpp, arcov-octfile.cpp
These two Octave wrappers were removed after on account of licensing considerations. The code followed appendix A ("Dynamically Linked Functions") of [Octave].
Provides some functionality as a Python extension module called 'ar'. This is perhaps the easiest way to start using these AR tools. Also demonstrates how working storage may be reused across multiple invocations to reduce the number of allocations for processing data sets.
A test driver for testing ar.hpp against benchmarks by [Bourke1998].
A test driver extracting a hierarchy of AR(p) models for a sample given by [Collomb2009].
A test driver solving a nonsymmetric, real-valued Toeplitz set of linear equations.
collomb2009.cpp, faber1986.cpp
For implementation testing and comparison purposes, a nearly verbatim copy of the recursive denominator algorithmic variant presented in [Kay1981,Faber1986] and [Collomb2009]. See comments at issue3.dat regarding numerical stability.

To aid investigating the behavior of the model selection and decorrelation routines for stationary chaotic systems, this is a flexible utility for outputting the (t, x, y, z) trajectory of the Lorenz attractor to standard output. This can be directly plotted, or manipulated using cut(1) and piped to arsel --subtract-mean. Try lorenz --help to see the available options.

For example, one can examine the long-time behavior of the Lorenz z coordinate using something akin to:

./lorenz --every=5 | cut -f 4 | ./arsel -ns | cut -s '-d ' -f 2-
test*.coeff, test*.dat
Sample data and exact parameters from [Bourke1998] used for make check.
rhoe.coeff, rhoe.dat
Sample turbulent total energy RMS fluctuation data and optimal parameters found by automatically by ARMASA [Broersen2002].
A large dataset from Nicholas Malaya generated by the Lorenz attractor. For AR(4) and higher order models, this data tickles an instability present in [Andersen1978]'s recursive denominator variant of Burg's algorithm. Namely, this variant will return a non-stationary process with complex poles outside the unit circle. See for details.
A derivation of some equations closely connected with the Yule--Walker system. Solving these permits recovering autocorrelations from process parameters.
A catalog of all implemented autoregressive model selection criteria.
The Lean Mean C++ Option Parser from which is used to parse command line arguments within sample applications.


If you find these tools useful towards publishing research, please consider citing:

-- [Oliver2014] Todd A. Oliver, Nicholas Malaya, Rhys Ulerich, and Robert D. Moser. "Estimating uncertainties in statistics computed from direct numerical simulation." Physics of Fluids 26 (March 2014): 035101+.


-- [Akaike1973] Akaike, Hirotugu. "Block Toeplitz Matrix Inversion." SIAM Journal on Applied Mathematics 24 (March 1973): 234-241.

-- [Andersen1978] Andersen, N. "Comments on the performance of maximum entropy algorithms." Proceedings of the IEEE 66 (November 1978): 1581-1582.

-- [Bernardo1976] Bernardo, J. M. "Algorithm AS 103: Psi (digamma) function." Journal of the Royal Statistical Society. Series C (Applied Statistics) 25 (1976).

-- [Bourke1998] Bourke, Paul. AutoRegression Analysis, November 1998.

-- [Box2008] Box, George E. P., Gwilym M. Jenkins, and Gregory C. Reinsel. Time Series Analysis : Forecasting and Control. 4 edition. John Wiley, June 2008.

-- [Broersen2000] Broersen, P. M. T. "Finite sample criteria for autoregressive order selection." IEEE Transactions on Signal Processing 48 (December 2000): 3550-3558.

-- [Broersen2002] Broersen, P. M. T. "Automatic spectral analysis with time series models." IEEE Transactions on Instrumentation and Measurement 51 (April 2002): 211-216.

-- [Broersen2006] Broersen, P. M. T. Automatic autocorrelation and spectral analysis. Springer, 2006.

-- [Bunch1985] Bunch, James R. "Stability of Methods for Solving Toeplitz Systems of Equations." SIAM Journal on Scientific and Statistical Computing 6 (1985): 349-364.

-- [Campbell1993] Campbell, W. and D. N. Swingler. "Frequency estimation performance of several weighted Burg algorithms." IEEE Transactions on Signal Processing 41 (March 1993): 1237-1247.

-- [Collomb2009] Cedrick Collomb. "Burg's method, algorithm, and recursion", November 2009.'s%20method,%20algorithm%20and%20recursion.pdf

-- [Faber1986] Faber, L. J. "Commentary on the denominator recursion for Burg's block algorithm." Proceedings of the IEEE 74 (July 1986): 1046-1047.

-- [GalassiGSL] M. Galassi et al, GNU Scientific Library Reference Manual (3rd Ed.), ISBN 0954612078. url{}

-- [Hurvich1989] Hurvich, Clifford M. and Chih-Ling Tsai. "Regression and time series model selection in small samples." Biometrika 76 (June 1989): 297-307.

-- [Ibrahim1987a] Ibrahim, M. K. "Improvement in the speed of the data-adaptive weighted Burg technique." IEEE Transactions on Acoustics, Speech, and Signal Processing 35 (October 1987): 1474–1476.

-- [Ibrahim1987b] Ibrahim, M. K. "On line splitting in the optimum tapered Burg algorithm." IEEE Transactions on Acoustics, Speech, and Signal Processing 35 (October 1987): 1476–1479.

-- [Ibrahim1989] Ibrahim, M. K. "Correction to 'Improvement in the speed of the data-adaptive weighted Burg technique'." IEEE Transactions on Acoustics, Speech, and Signal Processing 37 (1989): 128.

-- [Kahan1965] Kahan, W. "Further remarks on reducing truncation errors." Communications of the ACM 8 (January 1965): 40+.

-- [Kay1981] Kay, S. M. and S. L. Marple. "Spectrum analysis- A modern perspective." Proceedings of the IEEE 69 (November 1981): 1380-1419.

-- [Merchant1982] Merchant, G. and T. Parks. "Efficient solution of a Toeplitz-plus-Hankel coefficient matrix system of equations." IEEE Transactions on Acoustics, Speech, and Signal Processing 30 (February 1982): 40-44.

-- [Octave] Eaton, John W., David Bateman, and Søren Hauberg. GNU Octave Manual Version 3. Network Theory Limited, 2008.

-- [Press2007] Press, William H., Saul A. Teukolsky, William T. Vetterling, and Brian P. Flannery. Numerical recipes : The Art of Scientific Computing. Third edition. Cambridge University Press, September 2007.

-- [Seghouane2004] Seghouane, A. K. and M. Bekara. "A Small Sample Model Selection Criterion Based on Kullback's Symmetric Divergence." IEEE Transactions on Signal Processing 52 (December 2004): 3314-3323.

-- [vonStorch2001] Hans von Storch and Francis W. Zwiers. Statistical analysis in climate research. Cambridge University Press, March 2001. ISBN 978-0521012300.

-- [Thiebaux1984] Thiébaux, H. J. and F. W. Zwiers. "The Interpretation and Estimation of Effective Sample Size." J. Climate Appl. Meteor. 23 (May 1984): 800-811.;2

-- [Trenberth1984] Trenberth, K. E. "Some effects of finite sample size and persistence on meteorological statistics. Part I: Autocorrelations." Monthly Weather Review 112 (1984).;2

-- [Trench1967] Trench, William F. Weighting coefficients for the prediction of stationary time series from the finite past. SIAM J. Appl. Math. 15, 6 (Nov. 1967), 1502-1510.

-- [Vandevender1982] Vandevender, W. H. and K. H. Haskell. "The SLATEC mathematical subroutine library." ACM SIGNUM Newsletter 17 (September 1982): 16-21.

-- [Welford1962] Welford, B. P. "Note on a Method for Calculating Corrected Sums of Squares and Products." Technometrics 4 (1962).

-- [Zohar1974] Zohar, Shalhav. "The Solution of a Toeplitz Set of Linear Equations." J. ACM 21 (April 1974): 272-276.