CRAN Task View: Time Series Analysis
|Maintainer:||Rob J. Hyndman and Achim Zeileis|
|Contact:||Rob.Hyndman at monash.edu|
Base R ships with a lot of functionality useful for time series,
in particular in the stats package. This is complemented by many
packages on CRAN, which are briefly summarized below. There is also
a considerable overlap between the tools for time series and those
The packages in this view can be roughly
structured into the following topics. If you think that some package
is missing from the list, please let us know.
: Base R contains substantial infrastructure for representing and analyzing
time series data. The fundamental class is
represent regularly spaced time series (using numeric time stamps).
Hence, it is particularly well-suited for annual, monthly, quarterly
: Methods for analyzing and modeling time series include ARIMA models in
arima(), AR(p) and VAR(p) models in
structural models in
plot(), (partial) autocorrelation
decompose(), STL decomposition
stl(), moving average and autoregressive linear
filter(), and basic Holt-Winters forecasting in
Times and Dates
can only deal with numeric time stamps,
but many more classes are available for storing time/date information
and computing with it. For an overview see
R Help Desk: Date and
Time Classes in R
by Gabor Grothendieck and Thomas Petzoldt in
R News 4(1)
allow for more convenient computation with monthly
and quarterly observations, respectively.
from the base package is the basic class
for dealing with dates in daily data. The dates are internally stored
as the number of days since 1970-01-01.
package provides classes for
and date/time (intra-day) in
chron(). There is no
support for time zones and daylight savings time. Internally,
objects are (fractional) days since 1970-01-01.
the POSIX standard for date/time (intra-day) information and also support time zones
and daylight savings time. However, the time zone computations require
some care and might be system-dependent. Internally,
objects are the number of seconds since 1970-01-01 00:00:00 GMT.
provides functions that facilitate
certain POSIX-based computations.
is provided in the
package (previously: fCalendar). It is aimed at financial time/date information and deals with
time zones and daylight savings times via a new concept of "financial centers".
Internally, it stores all information in
all computations in GMT only. Calendar functionality, e.g., including
information about weekends and holidays for various stock exchanges,
is also included.
package provides the
class from the
facilitates computing with dates in terms of months.
package allows for the representation, manipulation and
visualisation of random time variables.
provides functions for aggregation of incomplete time series data.
Time Series Classes
As mentioned above,
is the basic class for
regularly spaced time series using numeric time stamps.
package provides infrastructure for regularly
and irregularly spaced time series using arbitrary classes for
the time stamps (i.e., allowing all classes from the previous section).
It is designed to be as consistent as possible with
Coercion from and to
is available for all other
classes mentioned in this section.
is based on
uniform handling of R's different time-based data classes.
Various packages implement irregular time series based on
time stamps, intended especially for financial applications. These include
implements time series with
time series with
contains infrastructure for setting
time frames in different formats.
Forecasting and Univariate Modeling
provides a class and methods for univariate time series
forecasts, and provides many functions implementing different
forecasting models including all those in the stats
provides some basic models with partial optimization,
package provides a larger set of
models and facilities with full optimization.
in stats (with
for subset AR models, and
for periodic autoregressive time series models.
in stats is the basic
function for ARIMA, SARIMA, ARIMAX, and subset ARIMA models.
It is enhanced in the
package provides different algorithms for ARMA and subset ARMA
implements a fast MLE algorithm for ARMA models.
Some facilities for fractional differenced ARFIMA
models are provided in the
handles estimation, diagnostics and forecasting for ARFIMA models.
contains functionality for generalized
SARIMA time series simulation. The
package handles multiplicative AR(1) with seasonal processes/
fits basic GARCH models,
implements ARIMA models with a wide class of GARCH innovations.
estimates a Bayesian GARCH(1,1) model with t innovations.
provides functions for simulating and fitting EGARCH models, and
implements Generalized Orthogonal GARCH (GO-GARCH) models.
The R-Forge package
aims to provide a flexible and rich GARCH modelling and testing environment. Its
has extensive information and examples.
contains methods for
linear time series analysis,
analysis of dynamic linear models,
series analysis and control,
bias-corrected forecasting and bootstrap prediction intervals
for autoregressive time series
for time series bootstrapping,
including block bootstrap with several variants.
fast stationary and block bootstrapping.
Maximum entropy bootstrap for time series is available in
Decomposition and Filtering
provides autoregressive and moving average linear filtering of
multiple univariate time series. The
package provides several robust time series filters, while
includes miscellaneous time series filters
useful for smoothing and extracting trend and cyclical
: Classical decomposition
is provided via
decompose(), more advanced and flexible
decomposition is available using
stl(), both from
the basic stats package.
includes computing wavelet filters, wavelet transforms and
multiresolution analyses. Wavelet methods for time series
analysis based on Percival and Walden (2000) are given in
wmtsa. Further wavelet methods can be found in
for real-time signal extraction
(direct filter approach).
for Bayesian inference on the discrete power
spectrum of time series.
provides Kolmogorov-Zurbenko Adaptive Filters including break detection, spectral analysis,
wavelets and KZ Fourier Transforms.
provides a fast implementation of Singular Spectrum Analysis for decomposition of a time series.
Stationarity, Unit Roots, and Cointegration
Stationarity and unit roots
various stationarity and unit root tests including
Augmented Dickey-Fuller, Phillips-Perron, and KPSS. Alternative
implementations of the ADF and KPSS tests are in the
package, which also includes further methods
such as Elliott-Rothenberg-Stock, Schmidt-Phillips and Zivot-Andrews
package also provides the MacKinnon test.
provides implementations of both the standard ADF
and a covariate-augmented ADF (CADF) test.
: The Engle-Granger two-step method with the Phillips-Ouliaris
cointegration test is implemented in
The latter additionally contains functionality for the Johansen trace
and lambda-max tests.
Nonlinear Time Series Analysis
: Various forms of nonlinear
autoregression are available in
additive AR, neural nets, SETAR and LSTAR models.
implements Bent-Cable autoregression.
provides Bayesian analysis of threshold
provided algorithms for time series analysis from
nonlinear dynamical systems theory.
an R interface to the algorithms and
provides an R implementation of the algorithms.
: Various tests for nonlinearity are provided in
Dynamic Regression Models
Dynamic linear models
: A convenient interface for fitting
dynamic regression models via OLS is available in
an enhanced approach that also works with other regression functions
and more time series classes is implemented in
package applies a dynamic variable selection procedure using an extension of the LARS
algorithm. More advanced dynamic system equations can be fitted using
dse. Gaussian linear state
space models can be fitted using
likelihood, Kalman filtering/smoothing and Bayesian methods).
models can be fitted using the
Distributed lag non-linear models
are handled via the
Multivariate Time Series Models
Vector autoregressive (VAR) models
are provided via
in the basic stats package including order
selection via the AIC. These models are
restricted to be stationary. Possibly non-stationary VAR models
are fitted in the
package, which also allows
VAR models in principal component space. More elaborate models
are provided in package
and a Bayesian approach is available in
uses fast implementations to estimate VAR models (possibly with
exogenous inputs and sparse coefficient matrices).
state space models
facilitates Monte Carlo experiments to
evaluate the associated estimation methods.
Vector error correction models
are available via the
packages, including versions with structural
Time series factor analysis
is provided in
Multivariate state space models
are implemented in the
(Fast Kalman Filter) package.
This provides relatively flexible state space models via the
parameters are allowed to be time-varying and intercepts are included in both equations.
An alternative implementation is provided by the
package which provides a
fast multivariate Kalman filter, smoother, simulation smoother and forecasting. Yet another implementation
is given in the
package which also contains tools for converting other multivariate models
into state space form. MARSS fits constrained and unconstrained multivariate autoregressive state-space models using an EM algorithm. All four packages assume the observational and state error terms are uncorrelated.
Partially-observed Markov processes
are a generalization of the usual linear multivariate state
space models, allowing non-Gaussian and non-linear models. These are implemented in the
Continuous time models
Continuous time autoregressive modelling
is provided in
Continuous time ARMA model
estimation and simulation is available in
Time Series Data
Data from Makridakis, Wheelwright and Hyndman (1998)
Forecasting: methods and
are provided in the
Data from Hyndman, Koehler, Ord and Snyder (2008)
with exponential smoothing
are in the
Data from the M-competition and M3-competition are provided in the
Data from Tsay (2005)
Analysis of financial
are in the
package, along with some functions and
script files required to work some of the examples.
provides a common interface to time series databases.
provides an interface for FAME time series databases
provides an interface to he Vhayu Velocity time series database
Economic and Financial Data Sets
both contain many data sets (including time series data)
from many econometrics text books
deseasonalize: Optimal deseasonalization for geophysical time series using AR fitting.
dtw: Dynamic time warping algorithms for computing and plotting pairwise alignments between time series.
ensembleBMA: Bayesian Model Averaging to create probabilistic forecasts from ensemble forecasts and weather observations.
fractal: Fractal time series modeling and analysis.
fractalrock: Generate fractal time series with non-normal returns distribution.
provide functions for analysing and modelling time series in hydrology and related environmental sciences.
GeneNet: Microarray time series and network analysis.
paleoTS: Modeling evolution in paleontological time series.
pastecs: Regulation, decomposition and analysis of space-time series.
ptw: Parametric time warping.
RSEIS: Seismic time series analysis tools.
season: Seasonal analysis of health data including regression models, time-stratified case-crossover, plotting functions and residual checks.
sde: Simulation and inference for stochastic differential equations.
tiger: Temporally resolved groups of typical differences (errors) between two time series are determined and visualized.
tsModel: Time series modeling for air pollution and health.
wavethresh: Locally stationary wavelet models for nonstationary time series
(including estimation, plotting, and simulation functionality for time-varying spectrums).
wq: Exploring water quality time series.