if (!requireNamespace("rmarkdown") || !rmarkdown::pandoc_available("1.12.3")) { warning(call. = FALSE, "These vignettes assume rmarkdown and pandoc version 1.12.3. These were not found. Older versions will not work.") knitr::knit_exit() }
knitr::opts_chunk$set(echo = TRUE)
State space models (SSM) are latent variable models which are commonly applied in analysing time series data due to their flexible and general framework [cf. @DK2012]. For R
[@r-core], there is large number of packages available for state space modelling, especially for the two special cases. First special case is linear-Gaussian SSM (LGSSM) where both the observation and state densities are Gaussian with linear relationships with the states. Another special case is SSM with discrete state space, which are sometimes called hidden Markov models (HMM). What is special about these two classes of models is that the marginal likelihood function, and the conditional state distributions (conditioned on the observations) of these models are analytically tractable, making inference relatively straightforward. See for example [@Petris2010, @Tusell2010, @KFAS, @seqHMM] for review of some of the R
packages dealing with these type of models. The R
package bssm
is designed for Bayesian inference of general state space models with non-Gaussian and/or non-linear observational and state equations. The package aims to provide easy-to-use and efficient functions for fully Bayesian inference of common time series models such basic structural time series model (BSM) [@Harvey1989] with exogenous covariates, simple stochastic volatility models, and discretized diffusion models, making it straightforward and efficient to make predictions and other inference in a Bayesian setting.
The motivation behind the bssm
package is in [@vihola-helske-franks] which suggests a new computationally efficient, parallelisable approach for Bayesian inference of state space models. The core idea is to use fast approximate Markov chain Monte Carlo (MCMC) targeting the approximate marginal posterior of the hyperparameters, which is then used in importance sampling type weighting phase which provides asymptotically exact samples from the joint posterior of hyperparameters and the hidden states. In addition to this the two-stage procedure, standard pseudo-marginal MCMC and so called delayed acceptance pseudo-marginal MCMC are also supported. For more details, see [@helske-vihola2021]. There is also separate vignette for nonlinear models as well as for discretized diffusion models.
Denote a sequence of observations $(y_1,\ldots,y_T)$ as $y_{1:T}$, and sequence of latent state variables $(\alpha_1,\ldots, \alpha_T)$ as $\alpha_{1:T}$. A general state space model consists of two parts: observation level densities $g_t(y_t | \alpha_t)$ and latent state transition densities $\mu_t(\alpha_{t+1} | \alpha_t)$. We first focus on the case where the state transitions are linear-Gaussian:
$$
\alpha_{t+1} = c_t + T_t \alpha_t + R_t \eta_t,
$$
where $c_t$ is known input vector (often omitted), and $T_t$ and $R_t$ are a system matrices which can depend on unknown parameters. Also, $\eta_t \sim N(0, I_k)$ and $\alpha_1 \sim N(a_1, P_1)$ independently of each other. For observation level density $g_t$, the bssm
package currently supports basic stochastic volatility model and general exponential family state space models.
For exponential family models, the observation equation has a general form
$$ g_t(y_t | d_t + Z_t \alpha_t, x'_t\beta, \phi, u_t), $$ where $d_t$ is a again known input, $x_t$ contains the exogenous covariate values at time $t$, with $\beta$ corresponding to the regression coefficients. Parameter $\phi$ and the known vector $u_t$ are distribution specific and can be omitted in some cases. Currently, following observational level distributions are supported:
Gaussian distribution: $g_t(y_t | Z_t \alpha_t, x'_t\beta) = x'_t \beta + Z_t \alpha_t + H_t \epsilon_t$ with $\epsilon_t \sim N(0, 1)$.
Poisson distribution: $g_t(y_t | Z_t \alpha_t, x'_t \beta, u_t) = \textrm{Poisson}(u_t \exp(x'_t \beta + Z_t \alpha_t))$, where $u_t$ is the known exposure at time $t$.
Binomial distribution: $g_t(y_t | Z_t \alpha_t, x'_t \beta, u_t) = \textrm{binomial}(u_t, \exp(x'_t \beta + Z_t \alpha_t) / (1 + \exp(x'_t \beta + Z_t \alpha_t)))$, where $u_t$ is the number of trials and $\exp(x_t \beta + Z_t \alpha_t) / (1 + \exp(x'_t \beta + Z_t \alpha_t))$ is the probability of the success.
Negative binomial distribution: $g_t(y_t | Z_t \alpha_t, x'_t \beta, \phi, u_t) = \textrm{negative binomial}(\exp(x'_t \beta + Z_t \alpha_t), \phi, u_t)$, where $u_t \exp(x'_t \beta + Z_t \alpha_t)$ is the expected value and $\phi$ is the dispersion parameter ($u_t$ is again exposure term).
Gamma distribution: $g_t(y_t | d_t + Z_t \alpha_t, \phi, u_t) = \textrm{Gamma}(\exp( d_t + Z_t \alpha_t), \phi, u_t)$, where $u_t \exp( d_t + Z_t \alpha_t)$ is the expected value, $\phi$ is the shape parameter, and $u_t$ is a known offset term.
For stochastic volatility model, there are two possible parameterizations available. In general for we have $$ y_t = x't\beta + \sigma \exp(\alpha_t / 2)\epsilon_t, \quad \epsilon_t \sim N(0, 1), $$ and $$ \alpha{t+1} = \mu + \rho (\alpha_t - \mu) + \sigma_{\eta} \eta_t, $$ with $\alpha_1 \sim N(\mu, \sigma^2_{\eta} / (1-\rho^2))$. For identifiability purposes we must either choose $\sigma=1$ or $\mu=0$. Although analytically identical, the parameterization with $\mu$ is often preferable in terms of computational efficiency.
Typically some of the model components such as $\beta$, $T_t$ or $R_t$ depend on unknown parameter vector $\theta$, so $g_t(y_t | \alpha_t)$ and $\mu_t(\alpha_{t+1} | \alpha_t)$ depend implicitly on $\theta$. Our goal is to perform Bayesian inference of the joint posterior of $\alpha_{1:T}$ and $\theta$.
For multivariate models, these distributions can be combined arbitrarily, except the stochastic volatility model case which is currently handled separately. Also, for fully Gaussian model, the observational level errors can be correlated, i.e. $Cov(\epsilon_t) = H_tH'_t$.
The general non-linear Gaussian model in the bssm
has following form:
$$
y_t = Z(t, \alpha_t, \theta) + H(t, \alpha_t, \theta)\epsilon_t,\
\alpha_{t+1} = T(t, \alpha_t, \theta) + R(t, \alpha_t, \theta)\eta_t,\
\alpha_1 \sim N(a_1(\theta), P_1(\theta)),
$$
with $t=1,\ldots, n$, $\epsilon_t \sim N(0,\textrm{I}_p)$, and $\eta \sim N(0,\textrm{I}_k)$. Here vector $\theta$ contains the unknown model parameters. Due to their generality and the need to repeated calls within MCMC, functions $T(\cdot)$, $H(\cdot)$, $T(\cdot)$, $R(\cdot)$,$a_1(\cdot)$, $P_1(\cdot)$, as well as functions defining the Jacobians of $Z(\cdot)$ and $T(\cdot)$ needed by the extended Kalman filter and the prior distribution for $\theta$ must be defined by user as a external pointers to user-defined C++
functions. All of these functions can also depend on some known parameters, defined as known_params
(vector) and known_tv_params
(matrix with $n$ columns) arguments to ssm_nlg
function. See the growth model vignette^[https://cran.r-project.org/package=bssm/vignettes/growth_model.html] for a template for these functions.
The bssm
package also supports models where the state equation is defined as a continuous time diffusion model of form
$$
\textrm{d} \alpha_t =
\mu(\alpha_t,\theta) \textrm{d} t +
\sigma(\alpha_t, \theta) \textrm{d} B_t, \quad t\geq0,
$$
where $B_t$ is a Brownian motion and where $\mu$ and $\sigma$ are scalar-valued functions, with the univariate observation density $g(y_k | \alpha_k)$ defined at integer times $k=1\ldots,n$. As these transition densities are generally unavailable for non-linear diffusions, we use Milstein time-discretisation scheme for approximate simulation with bootstrap particle filter. Fine discretisation mesh gives less bias than the coarser one, with increased computational complexity. These models are also defined via C++
snippets, see the SDE vignette for details.
Given the prior $p(\theta)$, the joint posterior of $\theta$ and $\alpha_{1:T}$ is given as
$$ p(\alpha_{1:T}, \theta | y_{1:T}) \propto p(\theta) p(\alpha_{1:T}, y_{1:T} | \theta) = p(\theta) p(y_{1:T} | \theta) p( \alpha_{1:T} | y_{1:T}, \theta) $$
where $p(y_{1:T} | \theta)$ is the marginal likelihood, and $p(\alpha_{1:T} | y_{1:T}, \theta)$ is often referred as a smoothing distribution. However, instead of targeting this joint posterior, it is typically more efficient to target the marginal posterior $p(\theta | y)$, and then given the sample ${\theta^{i}}^n_{i=1}$ from this marginal posterior, simulate states $\alpha^i_{1:T}$ from the smoothing distribution $p(\alpha_{1:T} | y_{1:T}, \theta^i)$ for $i=1\ldots,n$.
For Gaussian models given the parameters $\theta$, the marginal likelihood $p(y_{1:T} | \theta)$ can be computed using the well known Kalman filter recursions, and there are several algorithms for simulating the states $\alpha_{1:T}$ from the smoothing distribution $p(\alpha_{1:T} | y_{1:T})$ (see for example @DK2012). Therefore we can straightforwardly apply standard MCMC algorithms. In bssm
, we use an adaptive random walk Metropolis algorithm based on RAM [@Vihola2012] where we fix the target acceptance rate beforehand. There RAM algorithm is provided by the ramcmc
package [@helske-ram].
For non-linear/non-Gaussian models, the marginal likelihood $p(y_{1:T} | \theta)$ is typically not available in closed form. Thus we need to resort to simulation methods, which leads to pseudo-marginal MCMC algorithm [@lin-liu-sloan, @beaumont, @andrieu-roberts]. bssm
also supports more efficient inference algorithms based on (intermediate) approximations, see @helske-vihola2021 and @vihola-helske-franks.
Main functions of bssm
is written in C++
, with help of Rcpp
[@RcppA] and RcppArmadillo
[@RcppArmadillo] packages. On the R
side, package uses S3 methods in order to provide relatively unified workflow independent of the type of the model one is working with. The model building functions such as bsm_ng
and svm
are used to construct the actual state models which can be then passed to other methods, such as logLik
and run_mcmc
which compute the log-likelihood value and run MCMC algorithm respectively. We will now briefly describe the main functions and methods of bssm
, for more detailed descriptions of different function arguments and return values, see the corresponding documentation in R
.
For linear-Gaussian models, bssm
offers functions bsm_lg
for basic univariate structural time series models (BSM), ar1
for univariate, possibly noisy AR(1) process, as well as general ssm_ulg
and ssm_mlg
for arbitrary linear gaussian models. As an example, consider a Gaussian local linear trend model of form
$$
\begin{aligned}
y_t &= \mu_t + \epsilon_t,\
\mu_{t+1} &= \mu_t + \nu_t + \eta_t,\
\nu_{t+1} &= \nu_t + \xi_t,
\end{aligned}
$$
with zero-mean Gaussian noise terms $\epsilon_t, \eta_t, \xi_t$ with unknown standard deviations. This model can be built with bsm_lg
function as
library("bssm") data("nhtemp", package = "datasets") prior <- halfnormal(1, 10) bsm_model <- bsm_lg(y = nhtemp, sd_y = prior, sd_level = prior, sd_slope = prior)
Here we use helper function halfnormal
which defines half-Normal prior distribution for the standard deviation parameters, with first argument defining the initial value of the parameter, and second defines the scale parameter of the half-Normal distribution. Other prior options are normal
and uniform
.
For non-Gaussian models, function bsm_ng
can be used for constructing an BSM model where the observations are assumed to be distributed according to Poisson, binomial, negative binomial, or Gamma distribution. The syntax is nearly identical as in case of bsm_lg
, but we now define also the distribution via argument distribution
, and depending on the model, we can also define parameters u
and phi
. For Poisson and negative binomial models, the known parameter u
corresponds to the offset term, whereas in case of binomial model u
defines the number of trials. For negative binomial model, argument phi
defines the dispersion term, which can be given as a fixed value, or as a prior function. For same observational densities, a model where the state equation follows a first order autoregressive process can be defined using the function ng_ar1
. Finally, a stochastic volatility model can be defined using a function svm
, and an arbitrary linear-Gaussian state model with Poisson, binomial or negative binomial distributed observations can be defined with ssm_ung
and ssm_mng
for univariate and multivariate models respectively.
For models where the state equation is no longer linear-Gaussian, we can use our pointer-based C++ interface with the function ssm_nlg
. Diffusion models can be defined with the function ssm_sde
.
Filtering refers to estimating the conditional densities of the hidden states at time $t$, given the observations up to that point. For linear-Gaussian models, these densities can be efficiently computed using the Kalman filter recursions. The bssm
has a method kfilter
for this task. For models defined with the ssm_mng
,bsm_ng
, ar1_ng
, and svm
functions, kfilter
will first construct an approximating Gaussian model for which the Kalman filter is then used. For details of this approximation, see @durbin-koopman1997 and @vihola-helske-franks. For non-linear models defined by ssm_nlg
it is possible to perform filtering using extended Kalman filter (EKF) with the function ekf
, or unscented Kalman filter with the function ukf
. It is also possible to use iterated EKF (IEKF) by changing the argument iekf_iter
of the ekf
function. Compared to EKF, in IEKF the observation equation is linearized iteratively within each time step.
While Kalman filter solves the filtering problem exactly in case of linear-Gaussian models, EKF, UKF, and the filtering based on the approximating Gaussian models produce only approximate, possibly biased filtering estimates for general models. This problem can be solved by the use of particle filters (PF). These sequential Monte Carlo methods are computationally more expensive, but can in principle deal with almost arbitrary state space models. The bssm
supports general bootstrap particle filter (BSF) for all model classes of the bssm
. For ssm_mng
,bsm_ng
, ar1_ng
, and svm
models we recommend the particle filter called $\psi$-APF [@vihola-helske-franks] (see also another vignette on CRAN) which makes use of the previously mentioned approximating Gaussian model in order to produce more efficient filter. It is also available for ssm_nlg
models but in case of severe non-linearities, it is not necessarily best option.
Compared to filtering problem, in smoothing problems we are interested in the conditional densities of the hidden states at certain time point $t$ given all the observations $y_1,\ldots,y_t,\ldots,y_n$. Again for linear-Gaussian models we can use so called Kalman smoothing recursions, where as in case of more general models we can rely on approximating methods, or smoothing algorithms based on the output of particle filters. Currently only filter-smoother approach [@kitagawa] for particle smoothing is supported.
The main purpose of the bssm
is to allow efficient MCMC-based inference for various state space models. For this task, a method run_mcmc
can be used. Here we define a random walk model with a drift and stochastic seasonal component for UK gas consumption dataset and use 40 000 MCMC iteration where first half is discarded by default as a burn-in. Note that the number of iterations is quite low and in practice we should run the chain longer. Here we use less iterations to speed up the package checks on CRAN.
prior <- halfnormal(0.1, 1) UKgas_model <- bsm_lg(log10(UKgas), sd_y = prior, sd_level = prior, sd_slope = prior, sd_seasonal = prior) mcmc_bsm <- run_mcmc(UKgas_model, iter = 4e4, seed = 1) mcmc_bsm
Note that all MCMC algorithms of bssm
output also state forecasts for the timepoint $n + 1$, the summary statistics of this state is also shown in the output above.
Here we use ggplot2
[@ggplot2] package for the figures, so we transform the MCMC samples to data.frame
:
suppressMessages(library("ggplot2")) d <- as.data.frame(mcmc_bsm, variable = "theta") ggplot(d, aes(x = value)) + geom_density(adjust = 3, fill = "#92f0a8") + facet_wrap(~ variable, scales = "free") + theme_bw()
suppressMessages(library("dplyr")) d <- as.data.frame(mcmc_bsm, variable = "states") level_fit <- d %>% filter(variable == "level") %>% group_by(time) %>% summarise(consumption = mean(value), lwr = quantile(value, 0.025), upr = quantile(value, 0.975)) ggplot(level_fit, aes(x = time, y = consumption)) + geom_ribbon(aes(ymin = lwr, ymax = upr), fill = "#92f0a8", alpha = 0.25) + geom_line(colour = "#92f0a8") + geom_line(data = data.frame( consumption = log10(UKgas), time = time(UKgas)), colour = "grey30", linetype = "dashed") + theme_bw()
This work has been supported by the Academy of Finland research grants 284513, 312605, 311877, and 331817.
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