<CRANTaskView>

<name>TimeSeries</name>
<topic>Time Series Analysis</topic>
<maintainer email="Rob.Hyndman@monash.edu">Rob J Hyndman</maintainer>
<version>2016-09-09</version>

<info>
<p>
  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 in the <view>Econometrics</view> and <view>Finance</view> task views. 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.
</p>

<p><strong>Basics</strong></p>
<ul>
  <li><i>Infrastructure</i>: 
    Base R contains substantial infrastructure for representing and analyzing time series data. The fundamental class is <code>"ts"</code> that can represent regularly spaced time series (using numeric time stamps). Hence, it is particularly well-suited for annual, monthly, quarterly data, etc.
  </li>
  <li><i>Rolling statistics</i>: 
    Moving averages are computed by <code>ma</code> from <pkg>forecast</pkg>, and <code>rollmean</code> from <pkg>zoo</pkg>. The latter also provides a general function <code>rollapply</code>, along with other specific rolling statistics functions. 
    <pkg>roll</pkg>  provides parallel functions for computing rolling statistics.
  </li>
  <li><i>Graphics</i>: 
    Time series plots are obtained with <code>plot()</code> applied to <code>ts</code> objects. 
    (Partial) autocorrelation functions plots are implemented in <code>acf()</code> and <code>pacf()</code>. Alternative versions are provided by <code>Acf()</code> and <code>Pacf()</code> in <pkg>forecast</pkg>, along with a combination display using <code>tsdisplay()</code>. 
    <pkg>SDD</pkg> provides more general serial dependence diagrams, while <pkg>dCovTS</pkg> computes and plots the distance covariance and correlation functions of time series. 
    Seasonal displays are obtained using <code>monthplot()</code> in stats and <code>seasonplot</code> in <pkg>forecast</pkg>. 
    <pkg>Wats</pkg> implements wrap-around time series graphics. 
    <pkg>ggseas</pkg> provides ggplot2 graphics for seasonally adjusted series and rolling statistics.
    <pkg>dygraphs</pkg> provides an interface to the Dygraphs interactive time series charting library.  
    <pkg>ZRA</pkg> plots forecast objects from the <pkg>forecast</pkg> package using dygraphs.
    Basic fan plots of forecast distributions are provided by <pkg>forecast</pkg> and <pkg>vars</pkg>. More flexible fan plots of any sequential distributions are implemented in <pkg>fanplot</pkg>.
  </li>
</ul>

<p><strong>Times and Dates</strong></p>
<ul>
  <li>Class <code>"ts"</code> 
    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 <i>R Help Desk: Date and Time Classes in R</i> by Gabor Grothendieck and Thomas Petzoldt in <a href="http://CRAN.R-project.org/doc/Rnews/Rnews_2004-1.pdf">R News 4(1)</a>, 29-32.
  </li>
  <li>Classes <code>"yearmon"</code> and <code>"yearqtr"</code> 
    from <pkg>zoo</pkg> allow for more convenient computation with monthly and quarterly observations, respectively.
  </li>
  <li>Class <code>"Date"</code> 
    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.
  </li>
  <li>The <pkg>chron</pkg> package 
    provides classes for <code>dates()</code>, <code>hours()</code> and date/time (intra-day) in <code>chron()</code>. 
    There is no support for time zones and daylight savings time. 
    Internally, <code>"chron"</code> objects are (fractional) days since 1970-01-01.
  </li>
  <li>Classes <code>"POSIXct"</code> and <code>"POSIXlt"</code> 
    implement 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, <code>"POSIXct"</code> objects are the number of seconds since 1970-01-01 00:00:00 GMT.
    Package <pkg>lubridate</pkg> provides functions that facilitate certain POSIX-based computations.
  </li>
  <li>Class <code>"timeDate"</code> 
    is provided in the <pkg>timeDate</pkg> 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 <code>"POSIXct"</code> and does all computations in GMT only. 
    Calendar functionality, e.g., including information about weekends and holidays for various stock exchanges, is also included. 
  </li>
  <li>The <pkg>tis</pkg> package 
    provides the <code>"ti"</code> class for time/date information.
  </li>
  <li>The <code>"mondate"</code> class 
    from the <pkg>mondate</pkg> package facilitates computing with dates in terms of months.
  </li>
  <li>The <pkg>tempdisagg</pkg> package 
    includes methods for temporal disaggregation and interpolation of a low frequency time series to a higher frequency series.
  </li>
  <li><pkg>TimeProjection</pkg> 
    extracts useful time components of a date object, such as day of week, weekend, holiday, day of month, etc, and put it in a data frame.
  </li>
</ul>

<p><strong>Time Series Classes</strong></p>
<ul>
  <li>As mentioned above, <code>"ts"</code> 
    is the basic class for regularly spaced time series using numeric time stamps.
  </li>
  <li>The <pkg>zoo</pkg> 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 <code>"ts"</code>.
    Coercion from and to <code>"zoo"</code> is available for all other classes mentioned in this section.
  </li>
  <li>The package <pkg>xts</pkg> 
    is based on <pkg>zoo</pkg> and provides uniform handling of R's different time-based data classes.
  </li>
  <li>Various packages implement irregular time series 
    based on <code>"POSIXct"</code> time stamps, intended especially for financial applications. These include
    <code>"its"</code> from <pkg>its</pkg>, 
    <code>"irts"</code> from <pkg>tseries</pkg>, 
    and <code>"fts"</code> from <pkg>fts</pkg>.
  </li>
  <li>The class <code>"timeSeries"</code> 
    in <pkg>timeSeries</pkg> (previously: fSeries) implements time series with <code>"timeDate"</code> time stamps.
  </li>
  <li>The class <code>"tis"</code> 
    in <pkg>tis</pkg> implements time series with <code>"ti"</code> time stamps.
  </li>
  <li>The package <pkg>tframe</pkg> 
    contains infrastructure for setting time frames in different formats.
  </li>
</ul>

<p><strong>Forecasting and Univariate Modeling</strong></p>
<ul>
  <li>The <pkg>forecast</pkg> package
    provides a class and methods for univariate time series forecasts, and provides many functions implementing different forecasting models including all those in the stats package.
  </li>
  <li><i>Exponential smoothing</i>: 
    <code>HoltWinters()</code> in stats provides some basic models with partial optimization,
    <code>ets()</code> from the <pkg>forecast</pkg> package provides a larger set of models and facilities with full optimization. 
    The <pkg>MAPA</pkg> package combines exponential smoothing models at different levels of temporal aggregation to improve forecast accuracy.
  </li>
  <li>The theta method 
    is implemented in the <code>thetaf</code> function from the <pkg>forecast</pkg> package. 
    An alternative and extended implementation is provided in <pkg>forecTheta</pkg>.
  </li>
  <li><i>Autoregressive models</i>: 
    <code>ar()</code> in stats (with model selection) and <pkg>FitAR</pkg> for subset AR models.
  </li>
  <li><i>ARIMA models</i>: 
    <code>arima()</code> in stats is the basic function for ARIMA, SARIMA, ARIMAX, and subset ARIMA models.
    It is enhanced in the <pkg>forecast</pkg> package via the function <code>Arima()</code> along with <code>auto.arima()</code> for automatic order selection. 
    <code>arma()</code> in the <pkg>tseries</pkg> package provides different algorithms for ARMA and subset ARMA models. 
    <pkg>FitARMA</pkg> implements a fast MLE algorithm for ARMA models.
    Package <pkg>gsarima</pkg> contains functionality for Generalized SARIMA time series simulation. 
    The <pkg>mar1s</pkg> package handles multiplicative AR(1) with seasonal processes.
    <pkg>TSTutorial</pkg> provides an interactive tutorial for Box-Jenkins modelling.
    Improved prediction intervals for ARIMA and structural time series models are provided by <pkg>tsPI</pkg>.
  </li>
  <li><i>Periodic ARMA models</i>: 
    <pkg>pear</pkg> and <pkg>partsm</pkg> for periodic autoregressive time series models, and <pkg>perARMA</pkg> for periodic ARMA modelling and other procedures for periodic time series analysis.
  </li>
  <li><i>ARFIMA models</i>: 
    Some facilities for fractional differenced ARFIMA models are provided in the <pkg>fracdiff</pkg> package. 
    The <pkg>arfima</pkg> package has more advanced and general facilities for ARFIMA and ARIMA models, including dynamic regression (transfer function) models. 
    <code>armaFit()</code> from the <pkg>fArma</pkg> package is an interface for ARIMA and ARFIMA models. 
    Fractional Gaussian noise and simple models for hyperbolic decay time series are handled in the <pkg>FGN</pkg> package.
  </li>
  <li><i>Transfer function</i> models 
    are provided by the <code>arimax</code> function in the <pkg>TSA</pkg> package, and the <code>arfima</code> function in the <pkg>arfima</pkg> package. 
  </li>
  <li>Outlier detection 
    following the Chen-Liu approach is provided by <pkg>tsoutliers</pkg>.  
  </li>
  <li><i>Structural models</i> 
    are implemented in <code>StructTS()</code> in stats, and in <pkg>stsm</pkg> and <pkg>stsm.class</pkg>.
    <pkg>KFKSDS</pkg> provides a naive implementation of the Kalman filter and smoothers for univariate state space models. 
    Bayesian structural time series models are implemented in <pkg>bsts</pkg>
  </li>
  <li>Non-Gaussian time series 
    can be handled with GLARMA state space models via <pkg>glarma</pkg>, and using Generalized Autoregressive Score models in the <pkg>GAS</pkg> package. 
    Conditional Auto-Regression models  using Monte Carlo Likelihood methods are implemented in <pkg>mclcar</pkg>.
  </li>
  <li><i>GARCH models</i>: 
    <code>garch()</code> from <pkg>tseries</pkg> fits basic GARCH models.
    Many variations on GARCH models are provided by <pkg>rugarch</pkg>. 
    Other univariate GARCH packages include <pkg>fGarch</pkg> which implements ARIMA models with a wide class of GARCH innovations. 
    There are many more GARCH packages described in the <view>Finance</view> task view.
  </li>
  <li><i>Stochastic volatility</i> models 
    are handled by <pkg>stochvol</pkg> in a Bayesian framework. 
  </li>
  <li><i>Count time series</i> models 
    are handled in the <pkg>tscount</pkg> and <pkg>acp</pkg> packages. 
    <pkg>ZIM</pkg> provides for Zero-Inflated Models for count time series.  
    <pkg>tsintermittent</pkg> implements various models for analysing and forecasting intermittent demand time series. 
  </li>
  <li><i>Censored time series</i> 
   can be modelled using <pkg>cents</pkg> and <pkg>carx</pkg>.
  </li>
  <li><i>Portmanteau tests</i> 
    are provided via <code>Box.test()</code> in the stats package. 
    Additional tests are given by <pkg>portes</pkg> and <pkg>WeightedPortTest</pkg>.
  </li>
  <li><i>Change point detection</i> 
    is provided in <pkg>strucchange</pkg> (using linear regression models), 
    in <pkg>trend</pkg> (using nonparametric tests), 
    and in <pkg>wbsts</pkg> (using wild binary segmentation).
    The <pkg>changepoint</pkg> package provides many popular changepoint methods, 
    and <pkg>ecp</pkg> does nonparametric changepoint detection for univariate and multivariate series.
    Online change point detection for univariate and multivariate time series is provided by <pkg>onlineCPD</pkg>.
  </li>
  <li><i>Time series imputation</i> 
    is provided by the <pkg>imputeTS</pkg> package. 
    Some more limited facilities are available using <code>na.interp()</code> from the <pkg>forecast</pkg> package.
  </li>
  <li>Forecasts can be combined 
    using <pkg>ForecastCombinations</pkg> which supports the most frequently used methods to combine forecasts. 
    <pkg>forecastHybrid</pkg> provides functions for ensemble forecasts, combining approaches from the <pkg>forecast</pkg> package. 
    <pkg>opera</pkg> has facilities for online predictions based on combinations of forecasts provided by the user.
  </li>
  <li>Forecast evaluation
    is provided in the <code>accuracy()</code> function from <pkg>forecast</pkg>.
    Distributional forecast evaluation using scoring rules is available in <pkg>scoringRules</pkg>
  </li>
  <li><i>Miscellaneous</i>: 
    <pkg>ltsa</pkg> contains methods for linear time series analysis, 
    <pkg>timsac</pkg> for time series analysis and control, 
    and <pkg>tsbugs</pkg> for time series BUGS models.
  </li>
</ul>

<p><strong>Frequency analysis</strong></p>
<ul>
  <li><i>Spectral density estimation</i> 
    is provided by <code>spectrum()</code> in the stats package, including the periodogram, smoothed periodogram and AR estimates. 
    Bayesian spectral inference is provided by <pkg>bspec</pkg>.
    <pkg>quantspec</pkg> includes methods to compute and plot Laplace periodograms for univariate time series. 
    The Lomb-Scargle periodogram for unevenly sampled time series is computed by <pkg>lomb</pkg>.
    <pkg>psd</pkg> produces adaptive, sine-multitaper spectral density estimates.
    <pkg>kza</pkg> provides Kolmogorov-Zurbenko Adaptive Filters including break detection, spectral analysis, wavelets and KZ Fourier Transforms. 
    <pkg>multitaper</pkg> also provides some multitaper spectral analysis tools.
  </li>
  <li><i>Wavelet methods</i>: 
    The <pkg>wavelets</pkg> 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 <pkg>wmtsa</pkg>. 
    <pkg>biwavelet</pkg> can be used to plot and compute the wavelet spectra, cross-wavelet spectra, and wavelet coherence of non-stationary time series. It also includes functions to cluster time series based on the (dis)similarities in their spectrum. 
    Tests of white noise using wavelets are provided by <pkg>hwwntest</pkg>. 
    Further wavelet methods can be found in the packages <pkg>brainwaver</pkg>, <pkg>rwt</pkg>, <pkg>waveslim</pkg>, <pkg>wavethresh</pkg> and <pkg>mvcwt</pkg>.
  </li>
  <li><i>Harmonic regression</i> 
    using Fourier terms is implemented in <pkg>HarmonicRegression</pkg>. 
    The <pkg>forecast</pkg> package also provides some simple harmonic regression facilities via the <code>fourier</code> function.
  </li>
</ul>

<p><strong>Decomposition and Filtering</strong></p>
<ul>
  <li><i>Filters and smoothing</i>: 
    <code>filter()</code> in stats provides autoregressive and moving average linear filtering of multiple univariate time series. 
    The <pkg>robfilter</pkg> package provides several robust time series filters, 
    while <pkg>mFilter</pkg> includes miscellaneous time series filters useful for smoothing and extracting trend and cyclical components. 
    <code>smooth()</code> from the stats package computes Tukey's running median smoothers, 3RS3R, 3RSS, 3R, etc. 
    <pkg>sleekts</pkg> computes the 4253H twice smoothing method.
  </li>
  <li><i>Decomposition</i>: 
    Seasonal decomposition is discussed below.
    Autoregressive-based decomposition is provided by <pkg>ArDec</pkg>. 
    <pkg>rmaf</pkg> uses a refined moving average filter for decomposition.
  </li>
  <li><i>Singular Spectrum Analysis</i> 
    is implemented in <pkg>Rssa</pkg> and <pkg>spectral.methods</pkg>.
  </li>
  <li><i>Empirical Mode Decomposition</i> (EMD) 
    and Hilbert spectral analysis is provided by <pkg>EMD</pkg>. 
    Additional tools, including ensemble EMD, are available in <pkg>hht</pkg>. 
    An alternative implementation of ensemble EMD and its complete variant are available in <pkg>Rlibeemd</pkg>.
  </li>
</ul>

<p><strong>Seasonality</strong></p>
<ul>
  <li><i>Seasonal decomposition</i>: 
    the stats package provides classical decomposition in <code>decompose()</code>, and STL decomposition in <code>stl()</code>. 
    Enhanced STL decomposition is available in <pkg>stlplus</pkg>.
    <pkg>stR</pkg> provides Seasonal-Trend decomposition based on Regression.
  </li>
  <li> <pkg>x12</pkg> 
    provides a wrapper for the <a href="http://www.census.gov/srd/www/x12a/">X12 binaries</a> which must be installed first. 
    <pkg>x12GUI</pkg> provides a graphical user interface for <pkg>x12</pkg>. 
    X-13-ARIMA-SEATS binaries are provided in the <pkg>x13binary</pkg> package, 
    with <pkg>seasonal</pkg> providing an R interface.
  </li>
  <li><i>Analysis of seasonality</i>: 
    the <pkg>bfast</pkg> package provides methods for detecting and characterizing abrupt changes within the trend and seasonal components obtained from a decomposition. 
    <pkg>npst</pkg> provides a generalization of Hewitt's seasonality test.
  </li>
  <li><pkg>season</pkg>: 
    Seasonal analysis of health data including regression models, time-stratified case-crossover, plotting functions and residual checks.
  </li>
  <li><pkg>seas</pkg>: 
    Seasonal analysis and graphics, especially for climatology.
  </li>
  <li><pkg>deseasonalize</pkg>: 
    Optimal deseasonalization for geophysical time series using AR fitting.
  </li>
</ul>


<p><strong>Stationarity, Unit Roots, and Cointegration</strong></p>
<ul>
  <li><i>Stationarity and unit roots</i>: 
    <pkg>tseries</pkg> provides 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 <pkg>urca</pkg> package, which also includes further methods such as Elliott-Rothenberg-Stock, Schmidt-Phillips and Zivot-Andrews tests. 
    The <pkg>fUnitRoots</pkg> package also provides the MacKinnon test, 
    while <pkg>uroot</pkg> provides seasonal unit root tests.
    <pkg>CADFtest</pkg> provides implementations of both the standard ADF and a covariate-augmented ADF (CADF) test.
  </li>
  <li><i>Local stationarity</i>:  
    <pkg>locits</pkg> provides a test of local stationarity and computes the localized autocovariance. 
    Time series costationarity determination is provided by <pkg>costat</pkg>. 
    <pkg>LSTS</pkg> has functions for locally stationary time series analysis.
    Locally stationary wavelet models for nonstationary time series are implemented in <pkg>wavethresh</pkg> (including estimation, plotting, and simulation functionality for time-varying spectrums).
  </li>
  <li><i>Cointegration</i>: 
    The Engle-Granger two-step method with the Phillips-Ouliaris cointegration test is implemented in <pkg>tseries</pkg> 
    and <pkg>urca</pkg>. The latter additionally contains functionality for the Johansen trace and lambda-max tests.
    <pkg>tsDyn</pkg> provides Johansen's test and AIC/BIC simultaneous rank-lag selection.
    <pkg>CommonTrend</pkg> provides tools to extract and plot common trends from a cointegration system.
    Parameter estimation and inference in a cointegrating regression are implemented in <pkg>cointReg</pkg>.
  </li>
</ul>

<p><strong>Nonlinear Time Series Analysis</strong></p>
<ul>
  <li><i>Nonlinear autoregression</i>:
    Various forms of nonlinear autoregression are available in <pkg>tsDyn</pkg> including additive AR, neural nets, SETAR and LSTAR models, threshold VAR and VECM. Neural network autoregression is also provided in <pkg>GMDH</pkg>.
    <pkg>bentcableAR</pkg> implements Bent-Cable autoregression.
    <pkg>BAYSTAR</pkg> provides Bayesian analysis of threshold autoregressive models.
  </li>
  <li><pkg>tseriesChaos</pkg> 
    provides an R implementation of the algorithms from the <i><a href="http://www.mpipks-dresden.mpg.de/~tisean/">TISEAN</a> project</i>.
  </li>
  <li>Autoregression Markov switching models 
    are provided in <pkg>MSwM</pkg>, 
    while dependent mixtures of latent Markov models are given in <pkg>depmix</pkg> and <pkg>depmixS4</pkg> for categorical and continuous time series.
  </li>
  <li><i>Tests</i>: 
    Various tests for nonlinearity are provided in <pkg>fNonlinear</pkg>.
    <pkg>tseriesEntropy</pkg> tests for nonlinear serial dependence based on entropy metrics. 
  </li>
  <li>Additional functions for nonlinear time series 
    are available in <pkg>nlts</pkg> and <pkg>nonlinearTseries</pkg>.
  </li>
  <li>Fractal time series modeling and analysis 
    is provided by <pkg>fractal</pkg>. 
    <pkg>fractalrock</pkg> generates fractal time series with non-normal returns distributions.
  </li>
</ul>

<p><strong>Dynamic Regression Models</strong></p>
<ul>
  <li><i>Dynamic linear models</i>: 
    A convenient interface for fitting dynamic regression models via OLS is available in <pkg>dynlm</pkg>;
    an enhanced approach that also works with other regression functions and more time series classes is implemented in <pkg>dyn</pkg>.
    More advanced dynamic system equations can be fitted using <pkg>dse</pkg>.
    Gaussian linear state space models can be fitted using <pkg>dlm</pkg> (via maximum likelihood, Kalman filtering/smoothing and Bayesian methods), 
    or using <pkg>bsts</pkg> which uses MCMC.
    Functions for distributed lag non-linear modelling are provided in <pkg>dlnm</pkg>. 
  </li>
  <li><i>Time-varying parameter</i> models 
    can be fitted using the <pkg>tpr</pkg> package.
  </li>
  <li><pkg>orderedLasso</pkg> 
    fits a sparse linear model with an order constraint on the coefficients in order to handle lagged regressors where the coefficients decay as the lag increases.
  </li>
  <li>Dynamic modeling 
    of various kinds is available in <pkg>dynr</pkg> including discrete and continuous time, linear and nonlinear models, and different types of latent variables.
  </li>
</ul>

<p><strong>Multivariate Time Series Models</strong></p>
<ul>
  <li><i>Vector autoregressive (VAR) models</i> 
    are provided via <code>ar()</code> in the basic stats package including order selection via the AIC. These models are restricted to be stationary.
    <pkg>MTS</pkg> is an all-purpose toolkit for analyzing multivariate time series including VAR, VARMA, seasonal VARMA, VAR models with exogenous variables, multivariate regression with time series errors, and much more.
    Possibly non-stationary VAR models are fitted in the <pkg>mAr</pkg> package, which also allows VAR models in principal component space.
    <pkg>sparsevar</pkg> allows estimation of sparse VAR and VECM models,
    while <pkg>BigVAR</pkg> estimates VAR and VARX models with structured lasso penalties.
    Automated VAR models and networks are available in <pkg>autovarCore</pkg>.
    More elaborate models are provided in package <pkg>vars</pkg>, <pkg>tsDyn</pkg>, <code>estVARXls()</code> in <pkg>dse</pkg>, and a Bayesian approach is available in <pkg>MSBVAR</pkg>. 
    Another implementation with bootstrapped prediction intervals is given in <pkg>VAR.etp</pkg>.
    <pkg>mlVAR</pkg> provides multi-level vector autoregression. 
    <pkg>VARsignR</pkg> provides routines for identifying structural shocks in VAR models using sign restrictions.
  </li>
  <li><i>VARIMA models</i> and <i>state space models</i> 
    are provided in the <pkg>dse</pkg> package. 
    <pkg>EvalEst</pkg> facilitates Monte Carlo experiments to evaluate the associated estimation methods.
  </li>
  <li><i>Vector error correction models</i> 
    are available via the <pkg>urca</pkg>, <pkg>vars</pkg> and <pkg>tsDyn</pkg>
      packages, including versions with structural constraints and thresholding. 
  </li>
  <li><i>Time series component analysis</i>: 
    Time series factor analysis is provided in <pkg>tsfa</pkg>.
    <pkg>ForeCA</pkg> implements forecatable component analysis by searching for the best linear transformations that make a multivariate time series as forecastable as possible.
    <pkg>PCA4TS</pkg> finds a linear transformation of a multivariate time series giving lower-dimensional subseries that are uncorrelated with each other.
  </li>
  <li><i>Multivariate state space models</i> 
    are implemented in the <pkg>FKF</pkg> (Fast Kalman Filter) package. This provides relatively flexible state space models via the <code>fkf()</code> function: state-space parameters are allowed to be time-varying and intercepts are included in both equations.
    An alternative implementation is provided by the <pkg>KFAS</pkg> package which provides a fast multivariate Kalman filter, smoother, simulation smoother and forecasting. 
    Yet another implementation is given in the <pkg>dlm</pkg> package which also contains tools for converting other multivariate models into state space form.  
    <pkg>dlmodeler</pkg> provides a unified interface for <pkg>dlm</pkg>, <pkg>KFAS</pkg> and <pkg>FKF</pkg>.
    <pkg>MARSS</pkg> fits constrained and unconstrained multivariate autoregressive state-space models using an EM algorithm. 
    All of these packages assume the observational and state error terms are uncorrelated.
  </li>
  <li><i>Partially-observed Markov processes</i> 
    are a generalization of the usual linear multivariate state space models, allowing non-Gaussian and nonlinear models. These are implemented in the <pkg>pomp</pkg> package.
  </li>
  <li>Multivariate stochastic volatility models (using latent factors) are provided by <pkg>factorstochvol</pkg>.</li>
</ul>

<p><strong>Analysis of large groups of time series</strong></p>
<ul>
  <li><i>Time series clustering</i> 
    is implemented in <pkg>TSclust</pkg>, <pkg>dtwclust</pkg>, <pkg>BNPTSclust</pkg> and <pkg>pdc</pkg>.  
  </li>
  <li><pkg>TSdist</pkg> 
    provides distance measures for time series data.
  </li>
  <li><pkg>jmotif</pkg> 
    implements tools based on time series symbolic discretization for finding motifs in time series and facilitates interpretable time series classification.
  </li>
  <li>Methods for plotting and forecasting collections of hierarchical and grouped time series 
    are provided by <pkg>hts</pkg>. 
    An alternative approach to reconciling forecasts of hierarchical time series is provided by <pkg>gtop</pkg>.
  </li>
</ul>

<p><strong>Continuous time models</strong></p>
<ul>
	<li><i>Continuous time autoregressive modelling</i> 
    is provided in <pkg>cts</pkg>.
  </li>
  <li><pkg>Sim.DiffProc</pkg> 
    simulates and models stochastic differential equations.
  </li>
  <li>Simulation and inference for stochastic differential equations 
    is provided by <pkg>sde</pkg> and <pkg>yuima</pkg>.
  </li>
</ul>

<p><strong>Resampling</strong></p>
<ul>
  <li><i>Bootstrapping</i>: 
    The <pkg>boot</pkg> package provides function <code>tsboot()</code> for time series bootstrapping, including block bootstrap with several variants.
    <code>tsbootstrap()</code> from <pkg>tseries</pkg> provides fast stationary and block bootstrapping.
    Maximum entropy bootstrap for time series is available in <pkg>meboot</pkg>. 
    <pkg>timesboot</pkg> computes the bootstrap CI for the sample ACF and periodogram. 
    <pkg>BootPR</pkg> computes bias-corrected forecasting and boostrap prediction intervals for autoregressive time series.
  </li>
</ul>


<p><strong>Time Series Data</strong></p>
<ul>
  <li>Data from Makridakis, Wheelwright and Hyndman (1998) 
    <i>Forecasting: methods and applications</i> are provided in the <pkg>fma</pkg> package.
  </li>
  <li>Data from Hyndman, Koehler, Ord and Snyder (2008) 
    <i>Forecasting with exponential smoothing</i> are in the <pkg>expsmooth</pkg> package.
  </li>
  <li>Data from Hyndman and Athanasopoulos (2013) 
    <i>Forecasting: principles and practice</i> are in the <pkg>fpp</pkg> package.
  </li>
  <li>Data from the M-competition and M3-competition 
      are provided in the <pkg>Mcomp</pkg> package. Data from the M4 competition are given in <pkg>M4comp</pkg>.
  </li>
  <li><pkg>pdfetch</pkg> 
    provides facilities for downloading economic and financial time series from public sources.
  </li>
  <li>Data from the Quandl online portal 
      to financial, economical and social datasets can be queried interactively using the <pkg>Quandl</pkg> package.
  </li>
  <li>Data from the Datamarket online portal 
    can be fetched using the <pkg>rdatamarket</pkg> package.
  </li>
  <li>Data from Cryer and Chan (2010) 
    are in the <pkg>TSA</pkg> package.
  </li>
  <li>Data from Shumway and Stoffer (2011) 
    are in the <pkg>astsa</pkg> package.
  </li>
  <li>Data from Tsay (2005) 
      <i>Analysis of financial time series</i> are in the <pkg>FinTS</pkg> package, along with some functions and script files required to work some of the examples.
  </li>
  <li><pkg>TSdbi</pkg> 
    provides a common interface to time series databases.
  </li>
  <li><pkg>fame</pkg> 
    provides an interface for FAME time series databases
  </li>
  <li><pkg>AER</pkg> and <pkg>Ecdat</pkg> 
    both contain many data sets (including time series data) from many econometrics text books
  </li>
</ul>

<p><strong>Miscellaneous</strong></p>
<ul>
  <li><pkg>dtw</pkg>: 
    Dynamic time warping algorithms for computing and plotting pairwise alignments between time series.
  </li>
  <li><pkg>ensembleBMA</pkg>: 
    Bayesian Model Averaging to create probabilistic forecasts from ensemble forecasts and weather observations.
  </li>
  <li><pkg>earlywarnings</pkg>: 
    Early warnings signals toolbox for detecting critical transitions in time series
  </li>
  <li><pkg>events</pkg>: 
    turns machine-extracted event data into regular aggregated multivariate time series.
  </li>
  <li><pkg>FeedbackTS</pkg>: 
    Analysis of fragmented time directionality to investigate feedback in time series. 
  </li>
  <li><pkg>LPStimeSeries</pkg> 
    aims to find "learned pattern similarity" for time series.
  </li>
  <li><pkg>MAR1</pkg> 
    provides tools for preparing ecological community time series data for multivariate AR modeling.
  </li>
  <li><pkg>nets</pkg>: 
    routines for the estimation of sparse long run partial correlation networks for time series data.
  </li>
  <li><pkg>paleoTS</pkg>: 
    Modeling evolution in paleontological time series.
  </li>
  <li><pkg>pastecs</pkg>: 
    Regulation, decomposition and analysis of space-time series.
  </li>
  <li><pkg>ptw</pkg>: 
    Parametric time warping.
  </li>
  <li><pkg>RGENERATE</pkg> 
    provides tools to generate vector time series.
  </li>
  <li><pkg>RMAWGEN</pkg> 
    is set of S3 and S4 functions for spatial multi-site stochastic generation of daily time-series of temperature and precipitation making use of VAR models. The package can be used in climatology and statistical hydrology.
  </li>
  <li><pkg>RSEIS</pkg>: 
    Seismic time series analysis tools.
  </li>
  <li><pkg>rts</pkg>: 
    Raster time series analysis (e.g., time series of satellite images).
  </li>
  <li><pkg>sae2</pkg>: 
    Time series models for small area estimation.
  </li>
  <li><pkg>spTimer</pkg>: 
    Spatio-temporal Bayesian modelling.
  </li>
  <li><pkg>surveillance</pkg>: 
    Temporal and spatio-temporal modeling and monitoring of epidemic phenomena.
  </li>
  <li><pkg>TED</pkg>: 
    Turbulence time series Event Detection and classification.
  </li>
  <li><pkg>Tides</pkg>: 
    Functions to calculate characteristics of quasi periodic time series, e.g. observed estuarine water levels.
  </li>
  <li><pkg>tiger</pkg>: 
    Temporally resolved groups of typical differences (errors) between two time series are determined and visualized.
  </li>
  <li><pkg>TSMining</pkg>: 
    Mining Univariate and Multivariate Motifs in Time-Series Data.
  </li>
  <li><pkg>tsModel</pkg>: 
    Time series modeling for air pollution and health.
  </li>
</ul>

</info>

<packagelist>
  <pkg>acp</pkg>
  <pkg>AER</pkg>
  <pkg>ArDec</pkg>
  <pkg>arfima</pkg>
  <pkg>astsa</pkg>
  <pkg>autovarCore</pkg>
  <pkg>BAYSTAR</pkg>
  <pkg>bentcableAR</pkg>
  <pkg>bfast</pkg>
  <pkg>BigVAR</pkg>
  <pkg>biwavelet</pkg>
  <pkg>BNPTSclust</pkg>
  <pkg>boot</pkg>
  <pkg>BootPR</pkg>
  <pkg>brainwaver</pkg>
  <pkg>bspec</pkg>
  <pkg>bsts</pkg>
  <pkg>CADFtest</pkg>
  <pkg>carx</pkg>
  <pkg>cents</pkg>
  <pkg>changepoint</pkg>
  <pkg>chron</pkg>
  <pkg>cointReg</pkg>
  <pkg>CommonTrend</pkg>
  <pkg>costat</pkg>
  <pkg>cts</pkg>
  <pkg>dCovTS</pkg>
  <pkg>depmix</pkg>
  <pkg>depmixS4</pkg>
  <pkg>deseasonalize</pkg>
  <pkg>dlm</pkg>
  <pkg>dlmodeler</pkg>
  <pkg>dlnm</pkg>
  <pkg>dse</pkg>
  <pkg>dtw</pkg>
  <pkg>dtwclust</pkg>
  <pkg>dygraphs</pkg>
  <pkg>dyn</pkg>
  <pkg>dynlm</pkg>
  <pkg>dynr</pkg>
  <pkg>earlywarnings</pkg>
  <pkg>Ecdat</pkg>
  <pkg>ecp</pkg>
  <pkg>EMD</pkg>
  <pkg>ensembleBMA</pkg>
  <pkg>EvalEst</pkg>
  <pkg>events</pkg>
  <pkg>expsmooth</pkg>
  <pkg>factorstochvol</pkg>
  <pkg>fame</pkg>
  <pkg>fanplot</pkg>
  <pkg>fArma</pkg>
  <pkg>FeedbackTS</pkg>
  <pkg>fGarch</pkg>
  <pkg>FGN</pkg>
  <pkg>FinTS</pkg>
  <pkg>FitAR</pkg>
  <pkg>FitARMA</pkg>
  <pkg>FKF</pkg>
  <pkg>fma</pkg>
  <pkg>fNonlinear</pkg>
  <pkg>ForeCA</pkg>
  <pkg>ForecastCombinations</pkg>
  <pkg priority="core">forecast</pkg>
  <pkg>forecastHybrid</pkg>
  <pkg>forecTheta</pkg>
  <pkg>fpp</pkg>
  <pkg>fracdiff</pkg>
  <pkg>fractal</pkg>
  <pkg>fractalrock</pkg>
  <pkg>fts</pkg>
  <pkg>fUnitRoots</pkg>
  <pkg>GAS</pkg>
  <pkg>ggseas</pkg>
  <pkg>glarma</pkg>
  <pkg>GMDH</pkg>
  <pkg>gsarima</pkg>
  <pkg>gtop</pkg>
  <pkg>HarmonicRegression</pkg>
  <pkg>hht</pkg>
  <pkg>hts</pkg>
  <pkg>hwwntest</pkg>
  <pkg>imputeTS</pkg>
  <pkg>its</pkg>
  <pkg>jmotif</pkg>
  <pkg>KFAS</pkg>
  <pkg>KFKSDS</pkg>
  <pkg>kza</pkg>
  <pkg>locits</pkg>
  <pkg>lomb</pkg>
  <pkg>LPStimeSeries</pkg>
  <pkg>LSTS</pkg>
  <pkg>ltsa</pkg>
  <pkg>lubridate</pkg>
  <pkg>M4comp</pkg>
  <pkg>MAPA</pkg>
  <pkg>MAR1</pkg>
  <pkg>mar1s</pkg>
  <pkg>mAr</pkg>
  <pkg>MARSS</pkg>
  <pkg>mclcar</pkg>
  <pkg>Mcomp</pkg>
  <pkg>meboot</pkg>
  <pkg>mFilter</pkg>
  <pkg>mlVAR</pkg>
  <pkg>mondate</pkg>
  <pkg>MSBVAR</pkg>
  <pkg>MSwM</pkg>
  <pkg>MTS</pkg>
  <pkg>multitaper</pkg>
  <pkg>mvcwt</pkg>
  <pkg>nets</pkg>
  <pkg>nlts</pkg>
  <pkg>nonlinearTseries</pkg>
  <pkg>npst</pkg>
  <pkg>onlineCPD</pkg>
  <pkg>opera</pkg>
  <pkg>orderedLasso</pkg>
  <pkg>paleoTS</pkg>
  <pkg>partsm</pkg>
  <pkg>pastecs</pkg>
  <pkg>PCA4TS</pkg>
  <pkg>pdc</pkg>
  <pkg>pdfetch</pkg>
  <pkg>pear</pkg>
  <pkg>perARMA</pkg>
  <pkg>pomp</pkg>
  <pkg>portes</pkg>
  <pkg>psd</pkg>
  <pkg>ptw</pkg>
  <pkg>Quandl</pkg>
  <pkg>quantspec</pkg>
  <pkg>rdatamarket</pkg>
  <pkg>RGENERATE</pkg>
  <pkg>Rlibeemd</pkg>
  <pkg>rmaf</pkg>
  <pkg>RMAWGEN</pkg>
  <pkg>robfilter</pkg>
  <pkg>roll</pkg>
  <pkg>RSEIS</pkg>
  <pkg>Rssa</pkg>
  <pkg>rts</pkg>
  <pkg>rugarch</pkg>
  <pkg>rwt</pkg>
  <pkg>sae2</pkg>
  <pkg>scoringRules</pkg>
  <pkg>SDD</pkg>
  <pkg>sde</pkg>
  <pkg>seas</pkg>
  <pkg>season</pkg>
  <pkg>seasonal</pkg>
  <pkg>Sim.DiffProc</pkg>
  <pkg>sleekts</pkg>
  <pkg>sparsevar</pkg>
  <pkg>spectral.methods</pkg>
  <pkg>spTimer</pkg>
  <pkg>stlplus</pkg>
  <pkg>stochvol</pkg>
  <pkg>stR</pkg>
  <pkg>strucchange</pkg>
  <pkg>stsm.class</pkg>
  <pkg>stsm</pkg>
  <pkg>surveillance</pkg>
  <pkg>TED</pkg>
  <pkg>tempdisagg</pkg>
  <pkg>tframe</pkg>
  <pkg>Tides</pkg>
  <pkg>tiger</pkg>
  <pkg>timeDate</pkg>
  <pkg>TimeProjection</pkg>
  <pkg>timesboot</pkg>
  <pkg>timeSeries</pkg>
  <pkg>timsac</pkg>
  <pkg>tis</pkg>
  <pkg>tpr</pkg>
  <pkg>trend</pkg>
  <pkg>TSA</pkg>
  <pkg>tsbugs</pkg>
  <pkg>TSclust</pkg>
  <pkg>tscount</pkg>
  <pkg>TSdbi</pkg>
  <pkg>TSdist</pkg>
  <pkg>tsDyn</pkg>
  <pkg priority="core">tseries</pkg>
  <pkg>tseriesChaos</pkg>
  <pkg>tseriesEntropy</pkg>
  <pkg>tsfa</pkg>
  <pkg>tsintermittent</pkg>
  <pkg>TSMining</pkg>
  <pkg>tsModel</pkg>
  <pkg>tsoutliers</pkg>
  <pkg>tsPI</pkg>
  <pkg>TSTutorial</pkg>
  <pkg>urca</pkg>
  <pkg>uroot</pkg>
  <pkg>VAR.etp</pkg>
  <pkg>vars</pkg>
  <pkg>VARsignR</pkg>
  <pkg>Wats</pkg>
  <pkg>wavelets</pkg>
  <pkg>waveslim</pkg>
  <pkg>wavethresh</pkg>
  <pkg>wbsts</pkg>
  <pkg>WeightedPortTest</pkg>
  <pkg>wmtsa</pkg>
  <pkg>x12</pkg>
  <pkg>x12GUI</pkg>
  <pkg>x13binary</pkg>
  <pkg>xts</pkg>
  <pkg>yuima</pkg>
  <pkg>ZIM</pkg>
  <pkg>ZRA</pkg>
  <pkg priority="core">zoo</pkg>
</packagelist>

<links>
    <view>Finance</view>
    <view>Econometrics</view>
    <view>Environmetrics</view>
    <a href="http://data.is/TSDLdemo">Time Series Data Library</a>
    <a href="http://www.mpipks-dresden.mpg.de/~tisean/">TISEAN Project</a>
    <a href="http://www.quandl.com/">Quandl</a>
</links>
</CRANTaskView>
