# Superfast Likelihood Inference for Stationary Gaussian Time Series

#### 2022-02-24

This vignette illustrates the basic functionality of the SuperGauss package by simulating a few stochastic processes and estimating their parameters from regularly spaced data.

## Simulation of Fractional Brownian Motion

A one-dimensional fractional Brownian motion (fBM) $$X_t = X(t)$$ is a continuous Gaussian process with $$E[X_t] = 0$$ and $$\mathrm{cov}(X_t, X_s) = \tfrac 1 2 (|t|^{2H} + |s|^{2H} - |t-s|^{2H})$$, for $$0 < H < 1$$. fBM is not stationary but has stationary increments, such that $$(X_{t+\Delta t} - X_t) \stackrel{D}{=} (X_{s+\Delta t} - X_s)$$ for any $$s,t$$. As such, its covariance function is completely determined its mean squared displacement (MSD) $\mathrm{\scriptsize MSD}_X(t) = E[(X_t - X_0)^2] = |t|^{2H}.$ When the Hurst parameter $$H = \tfrac 1 2$$, fBM reduces to ordinary Brownian motion.

The following R code generates 5 independent fBM realizations of length $$N = 5000$$ with $$H = 0.3$$. The timing of the “superfast” method (Wood and Chan 1994) provided in this package is compared to that of a “fast” method (e.g., Brockwell and Davis 1991) and to the usual method (Cholesky decomposition of an unstructured variance matrix).

require(SuperGauss)

N <- 5000 # number of observations
dT <- 1/60 # time between observations (seconds)
H <- .3 # Hurst parameter

tseq <- (0:N)*dT # times at which to sample fBM
npaths <- 5 # number of fBM paths to generate

# to generate fbm, generate its increments, which are stationary
msd <- fbm_msd(tseq = tseq[-1], H = H)
acf <- msd2acf(msd = msd) # convert msd to acf

# superfast method
system.time({
dX <- rnormtz(n = npaths, acf = acf, fft = TRUE)
})
##    user  system elapsed
##   0.011   0.002   0.014
# fast method (about 3x as slow)
system.time({
rnormtz(n = npaths, acf = acf, fft = FALSE)
})
##    user  system elapsed
##   0.057   0.000   0.057
# unstructured variance method (much slower)
system.time({
matrix(rnorm(N*npaths), npaths, N) %*% chol(toeplitz(acf))
})
##    user  system elapsed
##  17.708   0.276  18.047
# convert increments to position measurements
Xt <- apply(rbind(0, dX), 2, cumsum)

# plot
clrs <- c("black", "red", "blue", "orange", "green2")
par(mar = c(4.1,4.1,.5,.5))
plot(0, type = "n", xlim = range(tseq), ylim = range(Xt),
xlab = "Time (s)", ylab = "Position (m)")
for(ii in 1:npaths) {
lines(tseq, Xt[,ii], col = clrs[ii], lwd = 2)
}

## Inference for the Hurst Parameter

Suppose that $$\boldsymbol{X}= (N_{X},\ldots,N_{)}$$ are equally spaced observations of an fBM process with $$X_i = X(i \Delta t)$$, and let $$\Delta\boldsymbol{X}= (N-1_{\Delta X},\ldots,N-1_{)}$$ denote the corresponding increments, $$\Delta X_i = X_{i+1} - X_i$$. Then the loglikelihood function for $$H$$ is $\ell(H \mid \Delta\boldsymbol{X}) = -\tfrac 1 2 \big(\Delta\boldsymbol{X}' \boldsymbol{V}_H^{-1} \Delta\boldsymbol{X}+ \log |\boldsymbol{V}_H|\big),$ where $$V_H$$ is a Toeplitz matrix, $\boldsymbol{V}_H= [\mathrm{cov}(\Delta X_i, \Delta X_j)]_{0 \le i,j < N} = \begin{bmatrix} \gamma_0 & \gamma_1 & \cdots & \gamma_{N-1} \\ \gamma_1 & \gamma_0 & \cdots & \gamma_{N-2} \\ \vdots & \vdots & \ddots & \vdots \\ \gamma_{N-1} & \gamma_{N-2} & \cdots & \gamma_0 \end{bmatrix}.$ Thus, each evaluation of the loglikelihood requires the inverse and log-determinant of a Toeplitz matrix, which scales as $$\mathcal O(N^2)$$ with the Durbin-Levinson algorithm. The SuperGauss package implements an extended version of the Generalized Schur algorithm of Ammar and Gragg (1988), which scales these computations as $$\mathcal O(N \log^2 N)$$. With careful memory management and extensive use of the FFTW library (Frigo and Johnson 2005), the SuperGauss implementation crosses over Durbin-Levinson at around $$N = 300$$.

### The Toeplitz Matrix Class

The bulk of the likelihood calculations in SuperGauss are handled by the Toeplitz matrix class. A Toeplitz object is created as follows:

# allocate and assign in one step
Tz <- Toeplitz$new(acf = acf) Tz ## Toeplitz matrix of size 5000 ## acf: 0.0857 -0.0208 -0.00421 -0.00228 -0.0015 -0.00109 ... # allocate memory only Tz <- Toeplitz$new(N = N)
Tz
## Toeplitz matrix of size 5000
##  acf:  NULL
Tz$set_acf(acf = acf) # assign later Its primary methods are illustrated below: all(acf == Tz$get_acf()) # extract acf
## [1] TRUE
# matrix multiplication
z <- rnorm(N)
x1 <- toeplitz(acf) %*% z # regular way
x2 <- Tz$prod(z) # with Toeplitz class x3 <- Tz %*% z # with Toeplitz class overloading the %*% operator range(x1-x2) ## [1] -1.526557e-15 1.665335e-15 range(x2-x3) ## [1] 0 0 # system of equations y1 <- solve(toeplitz(acf), z) # regular way y2 <- Tz$solve(z) # with Toeplitz class
y2 <- solve(Tz, z) # same thing but overloading solve()
range(y1-y2)
## [1] -1.280398e-11  8.704149e-12
# log-determinant
ld1 <- determinant(toeplitz(acf))$mod ld2 <- Tz$log_det() # with Toeplitz class
ld2 <- determinant(Tz) # same thing but overloading determinant()
# note: no $mod c(ld1, ld2) ## [1] -12737.89 -12737.89 ### Maximum Likelihood Calculation The following code shows how to obtain the maximum likelihood of $$H$$ and its standard error for a given fBM path. The log-PDF of the Gaussian with Toeplitz variance matrix is obtained either with SuperGauss::dnormtz(), or using the NormalToeplitz class. The advantage of the latter is that it does not reallocate memory for the underlying Toeplitz object at every likelihood evaulation. For speed comparisons, the optimization underlying the MLE calculation is done both using the superfast Generalized Schur algorithm and the fast Durbin-Levinson algorithm. dX <- diff(Xt[,1]) # obtain the increments of a given path N <- length(dX) # autocorrelation of fBM increments fbm_acf <- function(H) { msd <- fbm_msd(1:N*dT, H = H) msd2acf(msd) } # loglikelihood using generalized Schur algorithm NTz <- NormalToeplitz$new(N = N) # pre-allocate memory
loglik_GS <- function(H) {
NTz$logdens(z = dX, acf = fbm_acf(H)) } # loglikelihood using Durbin-Levinson algorithm loglik_DL <- function(H) { dnormtz(X = dX, acf = fbm_acf(H), method = "ltz", log = TRUE) } # superfast method system.time({ GS_mle <- optimize(loglik_GS, interval = c(.01, .99), maximum = TRUE) }) ## user system elapsed ## 0.067 0.003 0.070 # fast method (about 10x slower) system.time({ DL_mle <- optimize(loglik_DL, interval = c(.01, .99), maximum = TRUE) }) ## user system elapsed ## 0.851 0.006 0.857 c(GS = GS_mle$max, DL = DL_mle$max) ## GS DL ## 0.2950746 0.2950746 # standard error calculation require(numDeriv) ## Loading required package: numDeriv Hmle <- GS_mle$max
Hse <- -hessian(func = loglik_GS, x = Hmle) # observed Fisher Information
Hse <- sqrt(1/Hse[1])
c(mle = Hmle, se = Hse)
##         mle          se
## 0.295074650 0.002584653

### Caution with R6 Classes

In order to effectively manage memory in the underlying C++ code, the Toeplitz class is implemented using R6 classes. Among other things, this means that when a Toeplitz object is passed to a function, the function does not make a copy of it: all modifications to the object inside the object are reflected on the object outside the function as well, as in the following example:

T1 <- Toeplitz$new(N = N) T2 <- T1 # shallow copy: both of these point to the same memory location # affects both objects T1$set_acf(fbm_acf(.5))
T1
## Toeplitz matrix of size 5000
##  acf:  0.0167 0 1.73e-18 -3.47e-18 0 6.94e-18 ...
T2
## Toeplitz matrix of size 5000
##  acf:  0.0167 0 1.73e-18 -3.47e-18 0 6.94e-18 ...
fbm_logdet <- function(H) {
T1$set_acf(acf = fbm_acf(H)) T1$log_det()
}

# affects both objects
fbm_logdet(H = .3)
## [1] -12737.89
T1
## Toeplitz matrix of size 5000
##  acf:  0.0857 -0.0208 -0.00421 -0.00228 -0.0015 -0.00109 ...
T2
## Toeplitz matrix of size 5000
##  acf:  0.0857 -0.0208 -0.00421 -0.00228 -0.0015 -0.00109 ...

To avoid this behavior, it is necessary to make a deep copy of the object:

T3 <- T1$clone(deep = TRUE) T1 ## Toeplitz matrix of size 5000 ## acf: 0.0857 -0.0208 -0.00421 -0.00228 -0.0015 -0.00109 ... T3 ## Toeplitz matrix of size 5000 ## acf: 0.0857 -0.0208 -0.00421 -0.00228 -0.0015 -0.00109 ... # only affect T1 fbm_logdet(H = .7) ## [1] -29326.33 T1 ## Toeplitz matrix of size 5000 ## acf: 0.00324 0.00104 0.000612 0.000474 0.000397 0.000347 ... T3 ## Toeplitz matrix of size 5000 ## acf: 0.0857 -0.0208 -0.00421 -0.00228 -0.0015 -0.00109 ... ## Superfast Newton-Raphson In addition to the superfast algorithm for Gaussian likelihood evaluations, SuperGauss provides such algorithms for the loglikelihood gradient and Hessian functions, leading to superfast versions of many inference algorithms such as Newton-Raphson and Hamiltonian Monte Carlo. These are provided by the NormalToeplitz$grad() and NormalToeplitz$hess() methods. Both of these methods optionally return the lower order derivatives as well, reusing common computations to improve performance. An example of Newton-Raphson is given below using the two-parameter exponential autocorrelation model $\mathrm{\scriptsize ACF}_X(t \mid \lambda, \sigma) = \sigma^2 \exp(- |t/\lambda|).$ The example uses stats::nlm() for optimization, which requires the derivatives to be passsed as attributes to the (negative) loglikelihood. # autocorrelation function exp_acf <- function(t, lambda, sigma) sigma^2 * exp(-abs(t/lambda)) # gradient, returned as a 2-column matrix exp_acf_grad <- function(t, lambda, sigma) { ea <- exp_acf(t, lambda, 1) cbind(abs(t)*(sigma/lambda)^2 * ea, # d_acf/d_lambda 2*sigma * ea) # d_acf/d_sigma } # Hessian, returned as an array of size length(t) x 2 x 2 exp_acf_hess <- function(t, lambda, sigma) { ea <- exp_acf(t, lambda, 1) sl2 <- sigma/lambda^2 hess <- array(NA, dim = c(length(t), 2, 2)) hess[,1,1] <- sl2^2*(t^2 - 2*abs(t)*lambda) * ea # d2_acf/d_lambda^2 hess[,1,2] <- 2*sl2 * abs(t) * ea # d2_acf/(d_lambda d_sigma) hess[,2,1] <- hess[,1,2] # d2_acf/(d_sigma d_lambda) hess[,2,2] <- 2 * ea # d2_acf/d_sigma^2 hess } # simulate data lambda <- runif(1, .5, 2) sigma <- runif(1, .5, 2) tseq <- (1:N-1)*dT acf <- exp_acf(t = tseq, lambda = lambda, sigma = sigma) Xt <- rnormtz(acf = acf) NTz <- NormalToeplitz$new(N = N) # storage space

# negative loglikelihood function of theta = (lambda, sigma)
# include attributes for gradient and Hessian
exp_negloglik <- function(theta) {
lambda <- theta[1]
sigma <- theta[2]
# acf, its gradient, and Hessian
acf <- exp_acf(tseq, lambda, sigma)
d2acf <- exp_acf_hess(tseq, lambda, sigma)
# derivatives of NormalToeplitz up to order 2
derivs <- NTz$hess(z = Xt, dz = matrix(0, N, 2), d2z = array(0, dim = c(N, 2, 2)), acf = acf, dacf = dacf, d2acf = d2acf, full_out = TRUE) # negative loglikelihood with derivatives as attributes nll <- -1 * derivs$ldens
attr(nll, "gradient") <- -1 * derivs$grad attr(nll, "hessian") <- -1 * derivs$hess
nll
}

# optimization
system.time({
mle_fit <- nlm(f = exp_negloglik, p = c(1,1), hessian = TRUE)
})
##    user  system elapsed
##   0.431   0.009   0.439
# display estimates with standard errors
rbind(true = c(lambda = lambda, sigma = sigma),
est = mle_fit$estimate, se = sqrt(diag(solve(mle_fit$hessian))))
##         lambda      sigma
## true 1.3914065 0.71412532
## est  1.7019320 0.79757410
## se   0.3478682 0.08112464

## References

Ammar, G.S. and Gragg, W.B., 1988. Superfast solution of real positive definite Toeplitz systems. SIAM Journal on Matrix Analysis and Applications, 9 (1), 61–76.

Brockwell, P.J. and Davis, R.A., 1991. Time series: Theory and methods. Springer: New York.

Frigo, M. and Johnson, S.G., 2005. The design and implementation of FFTW3. Proceedings of the IEEE, 93 (2), 216–231.

Wood, A.T. and Chan, G., 1994. Simulation of stationary gaussian processes in $$[0, 1]^d$$. Journal of Computational and Graphical Statistics, 3 (4), 409–432.