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 Econometrics and Finance 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.

Basics

• Infrastructure : Base R contains substantial infrastructure for representing and analyzing time series data. The fundamental class is "ts" that can represent regularly spaced time series (using numeric time stamps). Hence, it is particularly well-suited for annual, monthly, quarterly data, etc.
• Graphics : Time series plots are obtained with plot() applied to ts objects. (Partial) autocorrelation functions plots are implemented in acf() and pacf(). Alternative versions are provided by Acf() and Pacf() in forecast, along with a combination display using tsdisplay(). SDD provides more general serial dependence diagrams. Seasonal displays are obtained using monthplot() in stats and seasonplot in forecast.

Times and Dates

• Class "ts" 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) , 29-32.
• Classes "yearmon" and "yearqtr" from zoo allow for more convenient computation with monthly and quarterly observations, respectively.
• Class "Date" 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.
• The chron package provides classes for dates(), hours() and date/time (intra-day) in chron(). There is no support for time zones and daylight savings time. Internally, "chron" objects are (fractional) days since 1970-01-01.
• Classes "POSIXct" and "POSIXlt" 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, "POSIXct" objects are the number of seconds since 1970-01-01 00:00:00 GMT. Package lubridate provides functions that facilitate certain POSIX-based computations.
• Class "timeDate" is provided in the timeDate 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 "POSIXct" and does all computations in GMT only. Calendar functionality, e.g., including information about weekends and holidays for various stock exchanges, is also included.
• The tis package provides the "ti" class for time/date information.
• The "mondate" class from the mondate package facilitates computing with dates in terms of months.
• The tempdisagg package includes methods for temporal disaggregation and interpolation of a low frequency time series to a higher frequency series.
• TimeProjection 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.

Time Series Classes

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

Forecasting and Univariate Modeling

• The forecast 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.
• Exponential smoothing : HoltWinters() in stats provides some basic models with partial optimization, ets() from the forecast package provides a larger set of models and facilities with full optimization. The MAPA package combines exponential smoothing models at different levels of temporal aggregation to improve forecast accuracy.
• Structural models are implemented in StructTS() in stats, and in stsm and stsm.class. KFKSDS provides a naive implementation of the Kalman filter and smoothers for univariate state space models. Bayesian structural time series models are implemented in bsts.
• Non-Gaussian time series can be handled with GLARMA state space models via the glarma package.
• Autoregressive models : ar() in stats (with model selection) and FitAR for subset AR models.
• ARIMA models : arima() in stats is the basic function for ARIMA, SARIMA, ARIMAX, and subset ARIMA models. It is enhanced in the forecast package via the function Arima() along with auto.arima() for automatic order selection. arma() in the tseries package provides different algorithms for ARMA and subset ARMA models. FitARMA implements a fast MLE algorithm for ARMA models. Package gsarima contains functionality for Generalized SARIMA time series simulation. The mar1s package handles multiplicative AR(1) with seasonal processes. TSTutorial provides an interactive tutorial for Box-Jenkins modelling.
• Periodic ARMA models : pear and partsm for periodic autoregressive time series models, and perARMA for periodic ARMA modelling and other procedures for periodic time series analysis.
• ARFIMA models : Some facilities for fractional differenced ARFIMA models are provided in the fracdiff package. The arfima package has more advanced and general facilities for ARFIMA and ARIMA models, including dynamic regression (transfer function) models. armaFit() from the fArma package is an interface for ARIMA and ARFIMA models. Fractional Gaussian noise and simple models for hyperbolic decay time series are handled in the FGN package.
• Transfer function models are provided by the arimax function in the TSA package, and the arfima function in the arfima package.
• Outlier detection following the Chen-Liu approach is provided by tsoutliers.
• GARCH models : garch() from tseries fits basic GARCH models. Many variations on GARCH models are provided by rugarch. Other univariate GARCH packages include fGarch which implements ARIMA models with a wide class of GARCH innovations, lgarch for log-GARCH models and bayesGARCH which estimates a Bayesian GARCH(1,1) model with t innovations. GEVStableGarch provides for ARMA-GARCH or ARMA-APARCH models with GEV and stable conditional distributions. gogarch implements Generalized Orthogonal GARCH (GO-GARCH) models.
• Stochastic volatility models are handled by stochvol in a Bayesian framework.
• tsintermittent implements various models for analysing and forecasting intermittent demand time series.
• Miscellaneous : ltsa contains methods for linear time series analysis, timsac for time series analysis and control, and tsbugs for time series BUGS models.

Frequency analysis

• Spectral density estimation is provided by spectrum() in the stats package, including the periodogram, smoothed periodogram and AR estimates. Bayesian spectral inference is provided by bspec. quantspec includes methods to compute and plot Laplace periodograms for univariate time series. The Lomb-Scargle periodogram for unevenly sampled time series is computed by lomb. psd produces adaptive, sine-multitaper spectral density estimates. kza provides Kolmogorov-Zurbenko Adaptive Filters including break detection, spectral analysis, wavelets and KZ Fourier Transforms.
• Wavelet methods : The wavelets 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. biwavelet 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. Further wavelet methods can be found in the packages brainwaver, rwt, waveslim, wavethresh and mvcwt.

Decomposition and Filtering

• Filters : filter() in stats provides autoregressive and moving average linear filtering of multiple univariate time series. The robfilter package provides several robust time series filters, while mFilter includes miscellaneous time series filters useful for smoothing and extracting trend and cyclical components.
• Decomposition : Classical decomposition is provided via decompose(), more advanced and flexible decomposition is available using stl(), both from the basic stats package. Autoregressive-based decomposition is provided by ArDec. rmaf uses a refined moving average filter for decomposition.
• Singular Spectrum Analysis is implemented in Rssa and spectral.methods.

Seasonality

• Seasonal decomposition : the stats package provides classical decomposition in decompose(), and STL decomposition in stl().
• x12 provides a wrapper for the X12 binaries which have to be installed first. x12GUI provides a graphical user interface for x12. An R interface to the later X-13ARIMA-SEATS binaries is given by seasonal.
• Analysis of seasonality : the bfast package provides methods for detecting and characterizing abrupt changes within the trend and seasonal components obtained from a decomposition. npst provides a generalization of Hewitt's seasonality test.
• season: Seasonal analysis of health data including regression models, time-stratified case-crossover, plotting functions and residual checks.
• seas: Seasonal analysis and graphics, especially for climatology.
• deseasonalize: Optimal deseasonalization for geophysical time series using AR fitting.

Stationarity, Unit Roots, and Cointegration

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

Nonlinear Time Series Analysis

• Nonlinear autoregression : Various forms of nonlinear autoregression are available in tsDyn including additive AR, neural nets, SETAR and LSTAR models, threshold VAR and VECM. Neural network autoregression is also provided in GMDH. bentcableAR implements Bent-Cable autoregression. BAYSTAR provides Bayesian analysis of threshold autoregressive models.
• tseriesChaos provides an R implementation of the algorithms from the TISEAN project .
• Autoregression Markov switching models are provided in MSwM, while dependent mixtures of latent Markov models are given in depmix and depmixS4 for categorical and continuous time series.
• Tests : Various tests for nonlinearity are provided in fNonlinear.
• Additional functions for nonlinear time series are available in nlts and nonlinearTseries.
• Fractal time series modeling and analysis is provided by fractal. fractalrock generates fractal time series with non-normal returns distributions.

Dynamic Regression Models

• Dynamic linear models : A convenient interface for fitting dynamic regression models via OLS is available in dynlm; an enhanced approach that also works with other regression functions and more time series classes is implemented in dyn. The tslars 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 dlm (via maximum likelihood, Kalman filtering/smoothing and Bayesian methods), or using bsts which uses MCMC. Functions for distributed lag non-linear modelling are provided in dlnm.
• Time-varying parameter models can be fitted using the tpr package.
• MIDAS regression and other econometric methods for mixed frequency time series data analysis are provided by midasr.
• gets provides GEneral-To-Specific (GETS) model selection for either ARX models with log-ARCH-X errors, or a log-ARCH-X model of the log variance.
• orderedLasso 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.

Multivariate Time Series Models

• Vector autoregressive (VAR) models are provided via ar() in the basic stats package including order selection via the AIC. These models are restricted to be stationary. MTS 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 mAr package, which also allows VAR models in principal component space. More elaborate models are provided in package vars, tsDyn, estVARXls() in dse, and a Bayesian approach is available in MSBVAR. Another implementation with bootstrapped prediction intervals is given in VAR.etp.
• VARIMA models and state space models are provided in the dse package. EvalEst facilitates Monte Carlo experiments to evaluate the associated estimation methods.
• Vector error correction models are available via the urca, vars and tsDyn packages, including versions with structural constraints and thresholding.
• Multivariate GARCH models including DCC, GO-GARCH and Copula-GARCH are available in rmgarch.
• Time series component analysis : Time series factor analysis is provided in tsfa. ForeCA implements forecatable component analysis by searching for the best linear transformations that make a multivariate time series as forecastable as possible.
• Multivariate state space models are implemented in the FKF (Fast Kalman Filter) package. This provides relatively flexible state space models via the fkf() function: state-space parameters are allowed to be time-varying and intercepts are included in both equations. An alternative implementation is provided by the KFAS package which provides a fast multivariate Kalman filter, smoother, simulation smoother and forecasting. Yet another implementation is given in the dlm package which also contains tools for converting other multivariate models into state space form. dlmodeler provides a unified interface for dlm, KFAS and FKF. MARSS 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.
• Partially-observed Markov processes are a generalization of the usual linear multivariate state space models, allowing non-Gaussian and nonlinear models. These are implemented in the pomp package.

Continuous time models

• Continuous time autoregressive modelling is provided in cts.
• Sim.DiffProc simulates and models stochastic differential equations.
• Simulation and inference for stochastic differential equations is provided by sde and yuima.

Resampling

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

Time Series Data

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

Miscellaneous

• 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.
• earlywarnings: Early warnings signals toolbox for detecting critical transitions in time series
• events: turns machine-extracted event data into regular aggregated multivariate time series.
• FeedbackTS: Analysis of fragmented time directionality to investigate feedback in time series.
• GeneCycle and GeneNet: Microarray time series and network analysis.
• hydroTSM and hydroGOF provide functions for analysing and modelling time series in hydrology and related environmental sciences.
• Interpol.T makes hourly interpolation of daily minimum and maximum temperature series. It is useful in climatology when hourly time-series must be downscaled from the daily information.
• LPStimeSeries aims to find "learned pattern similarity" for time series.
• MAR1 provides tools for preparing ecological community time series data for multivariate AR modeling.
• nets: routines for the estimation of sparse long run partial correlation networks for time series data.
• paleoTS: Modeling evolution in paleontological time series.
• pastecs: Regulation, decomposition and analysis of space-time series.
• portes: Portmanteau tests for univariate and multivariate time series models.
• ptw: Parametric time warping.
• PVAClone: Population growth models using data cloning.
• RMAWGEN 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.
• RSEIS: Seismic time series analysis tools.
• rts: Raster time series analysis (e.g., time series of satellite images).
• spTimer: Spatio-temporal Bayesian modelling.
• surveillance: Temporal and spatio-temporal modeling and monitoring of epidemic phenomena.
• TED: Turbulence time series Event Detection and classification.
• Tides: Functions to calculate characteristics of quasi periodic time series, e.g. observed estuarine water levels.
• tiger: Temporally resolved groups of typical differences (errors) between two time series are determined and visualized.
• TSclust: Time series clustering utilities.
• TSdist: Distance measures for time series data.
• tsModel: Time series modeling for air pollution and health.
• wq: Exploring water quality time series.
• WeightedPortTest provides weighted portmanteau statistics for time series goodness-of-fit testing.