Task view: Time Series Analysis

Last updated on 2021-12-20 by Rob J Hyndman

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.


  • 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.
  • Rolling statistics: Moving averages are computed by ma from forecast, and rollmean from zoo. The latter also provides a general function rollapply, along with other specific rolling statistics functions. slider calculates a diverse and comprehensive set of type-stable running functions for any R data types. tsibble provides slide() for rolling statistics, tile() for non-overlapping sliding windows, and stretch() for expanding windows. tbrf provides rolling functions based on date and time windows instead of n-lagged observations. roll provides parallel functions for computing rolling statistics. runner provides tools for running any R function in rolling windows or date windows. runstats provides fast computational methods for some running sample statistics. For data.table, froll() can be used for high-performance rolling statistics.
  • 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(). Seasonal displays are obtained using monthplot() in stats, seasonplot in forecast, and seasplot in tsutils. feasts and brolgar provide various time series graphics for tsibble objects including time plots, season plots, subseries plots, ACF and PACF plots, and some combination displays. Interactive graphics for tsibbles using htmlwidgets are provided by tsibbletalk. SDD provides more general serial dependence diagrams, while dCovTS computes and plots the distance covariance and correlation functions of time series. ggseas provides additional ggplot2 graphics for seasonally adjusted series and rolling statistics. Calendar plots are implemented in sugrrants. gravitas allows for visualizing probability distributions conditional on bivariate temporal granularities. dygraphs provides an interface to the Dygraphs interactive time series charting library. TSstudio provides some interactive visualization tools for time series. ZRA plots forecast objects from the forecast package using dygraphs. Basic fan plots of forecast distributions are provided by forecast and vars. More flexible fan plots of any sequential distributions are implemented in fanplot.

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, while clock provides a comprehensive library for date-time manipulations using a new family of orthogonal date-time classes (durations, time points, zoned-times, and calendars). timechange allows for efficient manipulation of date-times accounting for time zones and daylight saving times. wktmo converts weekly data to monthly data in several different ways.
  • 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.
  • Time series disaggregation is also provided by tsdisagg2 and disaggR.
  • 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".
  • The package xts is based on zoo and provides uniform handling of R's different time-based data classes.
  • Several packages aim to handle time-based tibbles: tsibble provides tidy temporal data frames and associated tools; tsbox contains tools for working with and coercing between many time series classes including tsibble, ts, xts, zoo and more. timetk is another toolkit for converting between various time series data classes.
  • Some manipulation tools for time series are available in data.table including shift() for lead/lag operations. Further basic time series functionalities are offered by DTSg which is based on data.table.
  • collapse provides fast computation of several time series functions such as lead/lag operations, (quasi-, log-) differences and growth rates on time-series and panel data, and ACF/PACF/CCF estimation for panel data.
  • Various packages implement irregular time series based on "POSIXct" time stamps, intended especially for financial applications. These include "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.
  • timeseriesdb manages time series for official statistics by mapping ts objects to PostgreSQL relations.

Forecasting and Univariate Modeling

  • The fable package provides tools for fitting univariate time series models to many series simultaneously including ETS, ARIMA, TSLM and other models. It also provides many functions for computing and analysing forecasts. The time series must be in the tsibble format. fabletools provides tools for extending the fable framework.
  • The forecast package provides similar tools for ts objects, while modeltime and modeltime.ensemble provides time series forecasting tools for use with the 'tidymodels' ecosystem.
  • Exponential smoothing: HoltWinters() in stats provides some basic models with partial optimization, ETS() from fable and ets() from forecast provide a larger set of models and facilities with full optimization. robets provides a robust alternative to the ets() function. smooth implements some generalizations of exponential smoothing. legion implements multivariate versions of exponential smoothing. The MAPA package combines exponential smoothing models at different levels of temporal aggregation to improve forecast accuracy. Some Bayesian extensions of exponential smoothing are contained in Rlgt.
  • prophet forecasts time series based on an additive model where nonlinear trends are fit with yearly and weekly seasonality, plus holidays. It works best with daily data. fable.prophet allows prophet models to be used in the fable framework.
  • The theta method is implemented in the THETA() function from fable, thetaf() function from forecast, and theta() from tsutils. An alternative and extended implementation is provided in forecTheta.
  • 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, RegARIMA, and subset ARIMA models. It is enhanced in the fable package via the ARIMA() function which allows for automatic modelling. Similar functionality is provided in the forecast package via the auto.arima() function. arma() in the tseries package provides different algorithms for ARMA and subset ARMA models. Other estimation methods including the innovations algorithm are provided by itsmr. FitARMA implements a fast MLE algorithm for ARMA models. Package gsarima contains functionality for Generalized SARIMA time series simulation. Robust ARIMA modeling is provided in the robustarima package. bayesforecast fits Bayesian time series models including seasonal ARIMA and ARIMAX models. BayesARIMAX implements Bayesian estimation of ARIMAX models. The mar1s package handles multiplicative AR(1) with seasonal processes. TSTutorial provides an interactive tutorial for Box-Jenkins modelling. Improved prediction intervals for ARIMA and structural time series models are provided by tsPI.
  • ARIMA models with multiple seasonal periods can be handled with tfarima and smooth.
  • Periodic ARMA models: partsm for periodic autoregressive time series models, and perARMA and pcts for periodic ARMA modelling and other procedures for periodic time series analysis.
  • Long memory 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. Additional methods for fitting and simulating non-stationary ARFIMA models are in nsarfima. LongMemoryTS provides a collection of functions for analysing long memory time series. Fractionally differenced Gegenbaur ARMA processes are handled by garma. esemifar provides tools for nonparametric smoothing of long-memory time series.
  • Transfer function models are provided by the arfima function in the arfima and the tfarima packages.
  • Structural (or unobserved component) models are implemented in StructTS() in stats and in stsm, while automatic modelling and forecasting are provided by UComp and autostsm. KFKSDS provides a naive implementation of the Kalman filter and smoothers for univariate state space models. statespacer implements univariate state space models including structural and SARIMA models. Bayesian structural time series models are implemented in bsts Robust Kalman filtering is provided by RobKF.
  • Non-Gaussian time series can be handled with GLARMA state space models via glarma, and using Generalized Autoregressive Score models in the GAS package. GlarmaVarSel provides variable selection in high-dimensional sparse GLARMA models. Conditional Auto-Regression models using Monte Carlo Likelihood methods are implemented in mclcar. Efficient Bayesian inference for nonlinear and non-Gaussian state space models is provided in bssm. Non-Gaussian state space models with exact marginal likelihood are given by NGSSEML.
  • 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. bayesforecast fits Bayesian time series models including several variations of GARCH models. There are many more GARCH packages described in the Finance task view.
  • Stochastic volatility models are handled by stochvol in a Bayesian framework.
  • Count time series models are handled in the tscount and acp packages. ZIM provides for Zero-Inflated Models for count time series. tsintermittent implements various models for analysing and forecasting intermittent demand time series.
  • Censored time series can be modelled using carx. ARCensReg fits univariate censored regression models with autoregressive errors.
  • Diffusion models such as Bass and Gompertz curves are provided by diffusion.
  • Portmanteau tests are provided via Box.test() in the stats package. Additional tests are given by portes, WeightedPortTest and testcorr.
  • Outlier detection following the Chen-Liu approach is provided by tsoutliers. The tsoutliers and tsclean functions in the forecast package provide some simple heuristic methods for identifying and correcting outliers. otsad implements a set of online anomaly detectors for time series. tsrobprep provides methods for replacing missing values and outliers using a model-based approach.
  • Change point detection is provided in strucchange and strucchangeRcpp (using linear regression models) and in trend (using nonparametric tests). The changepoint package provides many popular changepoint methods, and ecp does nonparametric changepoint detection for univariate and multivariate series. changepoint.np implements the nonparametric PELT algorithm, changepoint.mv detects changepoints in multivariate time series, while changepoint.geo implements the high-dimensional changepoint detection method GeomCP. VARDetect implements algorithms for detecting multiple changes in structural VAR models. InspectChangepoint uses sparse projection to estimate changepoints in high-dimensional time series. breakfast includes methods for fast multiple change-point detection and estimation.
  • Tests for possibly non-monotonic trends are provided by funtimes.
  • Time series imputation is provided by the imputeTS package. Some more limited facilities are available using na.interp() from the forecast package. imputeTestbench provides tools for testing and comparing imputation methods. mtsdi implements an EM algorithm for imputing missing values in multivariate normal time series, accounting for spatial and temporal correlations.
  • The seer package implements a framework for feature-based forecast model selection.
  • Forecasts can be combined in the fable package using simple linear expressions. ForecastComb supports many forecast combination methods including simple, geometric and regression-based combinations. forecastHybrid provides functions for ensemble forecasts, combining approaches from the forecast package. opera has facilities for online predictions based on combinations of forecasts provided by the user. profoc combines probabilistic forecasts using CRPS learning.
  • Point forecast evaluation is provided in the accuracy() function from the fable and forecast packages. Distributional forecast evaluation using scoring rules is available in fable, scoringRules and scoringutils. The Diebold-Mariano test for comparing the forecast accuracy of two models is implemented in the dm.test() function in forecast. A multivariate version of the Diebold-Mariano test is provided by multDM. tsutils implements the Nemenyi test for comparing forecasts. greybox provides ro() for general rolling origin evaluation of forecasts.
  • Tidy tools for forecasting are provided by sweep, converting objects produced in forecast to "tidy" data frames.
  • Multi-step-ahead direct forecasting with several machine learning approaches are provided in forecastML.
  • Miscellaneous: ltsa contains methods for linear time series analysis, timsac for time series analysis and control.

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 and regspec. 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. spectral uses Fourier and Hilbert transforms for spectral filtering. psd produces adaptive, sine-multitaper spectral density estimates. kza provides Kolmogorov-Zurbenko Adaptive Filters including break detection, spectral analysis, wavelets and KZ Fourier Transforms. multitaper also provides some multitaper spectral analysis tools. Higher-order spectral analysis is implemented in rhosa, including bispectrum, bicoherence, cross-bispectrum and cross-bicoherence.
  • Wavelet methods: The wavelets package includes computing wavelet filters, wavelet transforms and multiresolution analyses. Multiresolution forecasting using wavelets is also implemented in mrf. WaveletComp provides some tools for wavelet-based analysis of univariate and bivariate time series including cross-wavelets, phase-difference and significance tests. biwavelet is a port of the WTC Matlab package for univariate and bivariate wavelet analyses. mvLSW provides tools for multivariate locally stationary wavelet processes. Tests of white noise using wavelets are provided by hwwntest. Wavelet scalogram tools are contained in wavScalogram. Further wavelet methods can be found in the packages rwt, waveslim, wavethresh.
  • Harmonic regression using Fourier terms is implemented in HarmonicRegression. The fable and forecast packages also provide some simple harmonic regression facilities via the fourier function.

Decomposition and Filtering

  • Filters and smoothing: filter() in stats provides autoregressive and moving average linear filtering of multiple univariate time series. The robfilter package provides several robust time series filters. smooth() from the stats package computes Tukey's running median smoothers, 3RS3R, 3RSS, 3R, etc. sleekts computes the 4253H twice smoothing method. mFilter implements several filters for smoothing and extracting trend and cyclical components including Hodrick-Prescott and Butterworth filters. smoots provides nonparametric estimation of the time trend and its derivatives.
  • Decomposition: Seasonal decomposition is discussed below. Autoregressive-based decomposition is provided by ArDec. tsdecomp implements ARIMA-based decomposition of quarterly and monthly data. rmaf uses a refined moving average filter for decomposition.
  • Singular Spectrum Analysis is implemented in Rssa and ASSA.
  • Empirical Mode Decomposition (EMD) and Hilbert spectral analysis is provided by EMD. Additional tools, including ensemble EMD, are available in hht. An alternative implementation of ensemble EMD and its complete variant are available in Rlibeemd.


  • Seasonal decomposition: the stats package provides classical decomposition in decompose(), and STL decomposition in stl(). Enhanced STL decomposition is available in stlplus. stR provides Seasonal-Trend decomposition based on Regression. smooth and tsutils implement extended versions of classical decomposition.
  • X-13-ARIMA-SEATS binaries are provided in the x13binary package, with seasonal providing an R interface and seasonalview providing a GUI. An alternative interface is provided by x12.
  • An interface to the JDemetra+ seasonal adjustment software is provided by RJDemetra. ggdemetra provides associated ggplot2 functions.
  • Seasonal adjustment of daily time series, allowing for day-of-week, time-of-month, time-of-year and holiday effects is provided by dsa.
  • 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.
  • sazedR: Method to estimate the period of a seasonal time series.

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. uroot provides seasonal unit root tests. CADFtest provides implementations of both the standard ADF and a covariate-augmented ADF (CADF) test. MultipleBubbles tests for the existence of bubbles based on Phillips-Shi-Yu (2015).
  • Local stationarity: locits provides a test of local stationarity and computes the localized autocovariance. Time series costationarity determination is provided by costat. LSTS has functions for locally stationary time series analysis. Locally stationary wavelet models for nonstationary time series are implemented in wavethresh (including estimation, plotting, and simulation functionality for time-varying spectra).
  • 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. Parameter estimation and inference in a cointegrating regression are implemented in cointReg. nardl estimates nonlinear cointegrating autoregressive distributed lag models.

Nonlinear Time Series Analysis

  • Nonlinear autoregression: Tools for nonlinear time series analysis are provided in NTS including threshold autoregressive models, Markov-switching models, convolutional functional autoregressive models, and nonlinearity tests. 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. nnfor provides time series forecasting with neural networks. NlinTS includes neural network VAR, and a nonlinear version of the Granger causality test based on feedforward neural networks. bentcableAR implements Bent-Cable autoregression. BAYSTAR provides Bayesian analysis of threshold autoregressive models. Mixture AR models are implemented in mixAR and uGMAR.
  • tseriesChaos provides an R implementation of the algorithms from the TISEAN project. DChaos provides several algorithms for detecting chaotic signals inside univariate time series.
  • 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. tseriesEntropy tests for nonlinear serial dependence based on entropy metrics.
  • Additional functions for nonlinear time series are available in nlts and nonlinearTseries.


  • RTransferEntropy measures information flow between time series with Shannon and Renyi transfer entropy.
  • An entropy measure based on the Bhattacharya-Hellinger-Matusita distance is implemented in tseriesEntropy.
  • Various approximate and sample entropies are computed using TSEntropies.

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. 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. dLagM provides time series regression with distributed lags. Functions for distributed lag nonlinear modelling are provided in dlnm. sym.arma will fit ARMA models with regressors where the observations follow a conditional symmetric distribution.
  • Time-varying parameter models can be fitted using the tpr package.
  • greybox provides several tools for modelling and forecasting with dynamic regression models.

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. sparsevar allows estimation of sparse VAR and VECM models, bigtime estimates large sparse VAR, VARX and VARMA models, while BigVAR estimates VAR and VARX models with structured lasso penalties and svars implements data-driven structural VARs. Shrinkage estimation methods for VARs are implemented in VARshrink. More elaborate models are provided in package vars, tsDyn, estVARXls() in dse. Another implementation with bootstrapped prediction intervals is given in VAR.etp. bvartools assists in the set-up of Bayesian VAR models, while BMTAR implements Baysian Multivariate Threshold AR models with missing data. mfbvar includes tools for estimating mixed-frequency Bayesian VAR models. BVAR provides a toolkit for hierarchical Bayesian VAR models. BGVAR implements Bayesian Global VAR models. mlVAR provides multi-level vector autoregression. VARsignR provides routines for identifying structural shocks in VAR models using sign restrictions. gmvarkit estimates Gaussian mixture VAR models. GNAR provides methods for fitting network AR models, while graphicalVAR estimates graphical VAR models. gdpc implements generalized dynamic principal components. pcdpca extends dynamic principal components to periodically correlated multivariate time series. onlineVAR implements online fitting of time-adaptive lasso VARs. mgm estimates time-varying mixed graphical models and mixed VAR models via regularized regression.
  • VARIMA models and state space models are provided in the dse package.
  • Vector error correction models are available via the urca, ecm, vars, tsDyn packages, including versions with structural constraints and thresholding.
  • Vector exponential smoothing is provided by smooth.
  • Time series component analysis: ForeCA implements forecastable component analysis by searching for the best linear transformations that make a multivariate time series as forecastable as possible. PCA4TS finds a linear transformation of a multivariate time series giving lower-dimensional subseries that are uncorrelated with each other. HDTSA provides procedures for several high-dimensional time series analysis tools. One-sided dynamic principal components are computed in odpc. Frequency-domain-based dynamic PCA is implemented in freqdom. tsBSS provides blind source separation and supervised dimension reduction for time series.
  • Multivariate state space models An implementation is provided by the KFAS package which provides a fast multivariate Kalman filter, smoother, simulation smoother and forecasting. FKF provides a fast and flexible implementation of the Kalman filter, which can deal with missing values. FKF.SP implements fast Kalman filtering through sequential processing. Another implementation is given in the dlm package which also contains tools for converting other multivariate models into state space form. mssm also provides methods for multivariate state space models. MARSS fits constrained and unconstrained multivariate autoregressive state-space models using an EM algorithm. mbsts provides tools for multivariate Bayesian structural time series models. 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.
  • Multivariate stochastic volatility models (using latent factors) are provided by factorstochvol.

Analysis of large groups of time series

  • Time series features are computed in feasts for time series in tsibble format. They are computed using tsfeatures for a list or matrix of time series in ts format. In both packages, many built-in feature functions are included, and users can add their own. Rcatch22 provides fast computation of 22 features identified as particularly useful. fsMTS implements feature selection routines for multivariate time series.
  • Time series clustering is implemented in TSclust, dtwclust, BNPTSclust and pdc.
  • TSdist provides distance measures for time series data.
  • TSrepr includes methods for representing time series using dimension reduction and feature extraction.
  • rucrdtw provides R bindings for functions from the UCR Suite to enable ultrafast subsequence search for a best match under Dynamic Time Warping and Euclidean Distance. IncDTW provides incremental calculation of dynamic time warping for streaming time series.
  • Methods for plotting and forecasting collections of hierarchical and grouped time series are provided by fable and hts. thief uses hierarchical methods to reconcile forecasts of temporally aggregated time series. FoReco provides various forecast reconciliation methods for cross-sectional, temporal, and cross-temporal constrained time series. An alternative approach to reconciling forecasts of hierarchical time series is provided by gtop. ProbReco provides tools to train forecast reconciliation weights by optimizing probability scoring functions.

Functional time series

  • Tools for visualizing, modeling, forecasting and analysing functional time series are implemented in ftsa. NTS also implements functional autoregressive models. Seasonal functional autoregression models are provided by Rsfar. fpcb implements predictive confidence bands for functional time series.
  • fdaACF estimates the autocorrelation function for functional time series.
  • freqdom.fda provides implements of dynamical functional principal components for functional time series.
  • STFTS contains stationarity, trend and unit root tests for functional time series.
  • wwntests provides an array of white noise hypothesis tests for functional data.

Matrix and tensor-valued time series

  • tensorTS provides functions for estimation, simulation and prediction of factor and autoregressive models for matrix and tensor valued time series.

Continuous time models

  • carfima allows for continuous-time ARFIMA models.
  • Sim.DiffProc simulates and models stochastic differential equations.
  • Simulation and inference for stochastic differential equations is provided by sde and yuima.


  • Bootstrapping: The boot package provides function tsboot() for time series bootstrapping, including block bootstrap with several variants. blocklength allows for selecting the optimal block-length for a dependent bootstrap. 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 bootstrap prediction intervals for autoregressive time series. bootUR implements bootstrap unit root tests.

Time Series Data

  • Various data sets in tsibble format are provided by tsibbledata.
  • Data from Cryer and Chan (2010, 2nd ed) Time series analysis with applications in R are in the TSA package.
  • Data from Hyndman and Athanasopoulos (2018, 2nd ed) Forecasting: principles and practice are in the fpp2 package.
  • Data from Hyndman and Athanasopoulos (2021, 3rd ed) Forecasting: principles and practice are in the fpp3 package.
  • Data from Hyndman, Koehler, Ord and Snyder (2008) Forecasting with exponential smoothing are in the expsmooth package.
  • Data from Makridakis, Wheelwright and Hyndman (1998, 3rd ed) Forecasting: methods and applications are in the fma package.
  • Data from Shumway and Stoffer (2017, 4th ed) Time Series Analysis and Its Applications: With R Examples are in the astsa package.
  • Data from Tsay (2005, 2nd ed) Analysis of Financial Time Series are in the FinTS package.
  • Data from Woodward, Gray, and Elliott (2016, 2nd ed) Applied Time Series Analysis with R are in the tswge package.
  • AER and Ecdat both contain many data sets (including time series data) from many econometrics text books
  • Data from the M-competition and M3-competition are provided in the Mcomp package. Tcomp provides data from the 2010 IJF Tourism Forecasting Competition.
  • National time series data: readabs downloads, imports and tidies time series data from the Australian Bureau of Statistics. BETS provides access to the most important economic time series in Brazil. bundesbank allows access to the time series databases of the German central bank. Data from Switzerland via dataseries.org can be downloaded and imported using dataseries. ugatsdb provides an API to access time series data for Uganda.
  • Time series data bases: fame provides an interface for FAME time series databases. Economic time series and other data from FRED (the Federal Reserve Economic Data) can be retrieved using fredr. rdbnomics provides access to hundreds of millions of time series from DBnomics. influxdbr provides an interface to the InfluxDB time series database. 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. TSdbi provides a common interface to time series databases. tsdb implements a simple data base for numerical time series.
  • Synthetic data are produced by simulate() in forecast package or generate() in fable, given a specific model. gratis generates new time series with diverse and controllable characteristics using mixture autoregression models. synthesis generates synthetic time series from commonly used statistical models, including linear, nonlinear and chaotic systems. tssim flexibly simulates daily or monthly time series using seasonal, calendar, and outlier components.


  • dtw: Dynamic time warping algorithms for computing and plotting pairwise alignments between time series.
  • EBMAforecast: Ensemble Bayesian model averaging forecasts using Gibbs sampling or EM algorithms.
  • 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
  • FeedbackTS: Analysis of fragmented time directionality to investigate feedback in time series.
  • gsignal is an R implementation of the Octave package "signal", containing a variety of signal processing tools.
  • ifultools is a collection of efficient signal processing, image processing, and time series modeling routines.
  • imputePSF: imputes missing data using pattern sequences.
  • LPStimeSeries aims to find "learned pattern similarity" for time series.
  • 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.
  • PSF: Forecasting univariate time series using pattern-sequences.
  • ptw: Parametric time warping.
  • RGENERATE provides tools to generate vector time series.
  • 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.
  • 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.
  • tsfknn: Time series forecasting with k-nearest-neighbours.
  • TSMining: Mining Univariate and Multivariate Motifs in Time-Series Data.
  • tsModel: Time series modeling for air pollution and health.


acp — 2.1

Autoregressive Conditional Poisson

AER — 1.2-9

Applied Econometrics with R

ARCensReg — 2.1

Fitting Univariate Censored Linear Regression Model with Autoregressive Errors

ArDec — 2.0

Time series autoregressive-based decomposition

arfima — 1.7-0

Fractional ARIMA (and Other Long Memory) Time Series Modeling

ASSA — 2.0

Applied Singular Spectrum Analysis (ASSA)

astsa — 1.14

Applied Statistical Time Series Analysis

autostsm — 2.1.1

Automatic Structural Time Series Models

BayesARIMAX — 0.1.1

Bayesian Estimation of ARIMAX Model

bayesforecast — 1.0.1

Bayesian Time Series Modeling with Stan

BAYSTAR — 0.2-9

On Bayesian analysis of Threshold autoregressive model (BAYSTAR)

bentcableAR — 0.3.0

Bent-Cable Regression for Independent Data or Autoregressive Time Series

BETS — 0.4.9

Brazilian Economic Time Series

bfast — 1.6.1

Breaks for Additive Season and Trend

BGVAR — 2.4.3

Bayesian Global Vector Autoregressions

bigtime — 0.2.1

Sparse Estimation of Large Time Series Models

BigVAR — 1.0.6

Dimension Reduction Methods for Multivariate Time Series

biwavelet — 0.20.21

Conduct Univariate and Bivariate Wavelet Analyses

blocklength — 0.1.4

Select an Optimal Block-Length to Bootstrap Dependent Data (Block Bootstrap)

BMTAR — 0.1.1

Bayesian Approach for MTAR Models with Missing Data

BNPTSclust — 2.0

A Bayesian Nonparametric Algorithm for Time Series Clustering

boot — 1.3-28

Bootstrap Functions (Originally by Angelo Canty for S)

BootPR — 0.60

Bootstrap Prediction Intervals and Bias-Corrected Forecasting

bootUR — 0.4.2

Bootstrap Unit Root Tests

breakfast — 2.2

Methods for Fast Multiple Change-Point Detection and Estimation

brolgar — 0.1.2

Browse Over Longitudinal Data Graphically and Analytically in R

bspec — 1.5

Bayesian Spectral Inference

bssm — 2.0.0

Bayesian Inference of Non-Linear and Non-Gaussian State Space Models

bsts — 0.9.7

Bayesian Structural Time Series

bundesbank — 0.1-9

Download Data from Bundesbank

BVAR — 1.0.2

Hierarchical Bayesian Vector Autoregression

bvartools — 0.2.1

Bayesian Inference of Vector Autoregressive and Error Correction Models

CADFtest — 0.3-3

A Package to Perform Covariate Augmented Dickey-Fuller Unit Root Tests

carfima — 2.0.2

Continuous-Time Fractionally Integrated ARMA Process for Irregularly Spaced Long-Memory Time Series Data

carx — 0.7.1

Censored Autoregressive Model with Exogenous Covariates

changepoint.geo — 1.0.1

Geometrically Inspired Multivariate Changepoint Detection

changepoint.mv — 1.0.2

Changepoint Analysis for Multivariate Time Series

changepoint.np — 1.0.3

Methods for Nonparametric Changepoint Detection

changepoint — 2.2.2

Methods for Changepoint Detection

chron — 2.3-56

Chronological Objects which can Handle Dates and Times

clock — 0.6.0

Date-Time Types and Tools

cointReg — 0.2.0

Parameter Estimation and Inference in a Cointegrating Regression

collapse — 1.7.2

Advanced and Fast Data Transformation

costat — 2.4

Time Series Costationarity Determination

data.table — 1.14.2

Extension of `data.frame`

dataseries — 0.2.0

Switzerland's Data Series in One Place

DChaos — 0.1-6

Chaotic Time Series Analysis

dCovTS — 1.2

Distance Covariance and Correlation for Time Series Analysis

depmix — 0.9.16

Dependent Mixture Models

depmixS4 — 1.5-0

Dependent Mixture Models - Hidden Markov Models of GLMs and Other Distributions in S4

deseasonalize — 1.35

Optimal deseasonalization for geophysical time series using AR fitting

diffusion — 0.2.7

Forecast the Diffusion of New Products

disaggR — 1.0.2

Two-Steps Benchmarks for Time Series Disaggregation

dLagM —

Time Series Regression Models with Distributed Lag Models

dlm — 1.1-5

Bayesian and Likelihood Analysis of Dynamic Linear Models

dlnm — 2.4.7

Distributed Lag Non-Linear Models

dsa — 1.0.12

Seasonal Adjustment of Daily Time Series

dse — 2020.2-1

Dynamic Systems Estimation (Time Series Package)

DTSg — 0.8.1

A Class for Working with Time Series Data Based on 'data.table' and 'R6' with Largely Optional Reference Semantics

dtw — 1.22-3

Dynamic Time Warping Algorithms

dtwclust — 5.5.7

Time Series Clustering Along with Optimizations for the Dynamic Time Warping Distance

dygraphs —

Interface to 'Dygraphs' Interactive Time Series Charting Library

dyn — 0.2-9.6

Time Series Regression

dynlm — 0.3-6

Dynamic Linear Regression

earlywarnings — 1.0.59

Early Warning Signals Toolbox for Detecting Critical Transitions in Timeseries

EBMAforecast — 1.0.2

Estimate Ensemble Bayesian Model Averaging Forecasts using Gibbs Sampling or EM-Algorithms

Ecdat — 0.3-9

Data Sets for Econometrics

ecm — 6.3.0

Build Error Correction Models

ecp — 3.1.3

Non-Parametric Multiple Change-Point Analysis of Multivariate Data

EMD — 1.5.9

Empirical Mode Decomposition and Hilbert Spectral Analysis

ensembleBMA — 5.1.7

Probabilistic Forecasting using Ensembles and Bayesian Model Averaging

esemifar — 1.0.1

Smoothing Long-Memory Time Series

expsmooth — 2.3

Data Sets from "Forecasting with Exponential Smoothing"

fable — 0.3.1

Forecasting Models for Tidy Time Series

fable.prophet — 0.1.0

Prophet Modelling Interface for 'fable'

fabletools — 0.3.2

Core Tools for Packages in the 'fable' Framework

factorstochvol — 1.0.1

Bayesian Estimation of (Sparse) Latent Factor Stochastic Volatility Models

fame — 2.21.1

Interface for FAME Time Series Database

fanplot — 4.0.0

Visualisation of Sequential Probability Distributions Using Fan Charts

fdaACF — 1.0.0

Autocorrelation Function for Functional Time Series

feasts — 0.2.2

Feature Extraction and Statistics for Time Series

FeedbackTS — 1.5

Analysis of Feedback in Time Series

fGarch — 3042.83.2

Rmetrics - Autoregressive Conditional Heteroskedastic Modelling

FinTS — 0.4-6

Companion to Tsay (2005) Analysis of Financial Time Series

FitAR — 1.94

Subset AR Model Fitting

FitARMA — 1.6.1

Fit ARMA or ARIMA Using Fast MLE Algorithm

FKF — 0.2.3

Fast Kalman Filter

FKF.SP — 0.1.3

Fast Kalman Filtering Through Sequential Processing

fma — 2.4

Data Sets from "Forecasting: Methods and Applications" by Makridakis, Wheelwright & Hyndman (1998)

fNonlinear — 3042.79

Rmetrics - Nonlinear and Chaotic Time Series Modelling

ForeCA — 0.2.7

Forecastable Component Analysis

FoReco — 0.2.1

Point Forecast Reconciliation

forecast — 8.16

Forecasting Functions for Time Series and Linear Models

ForecastComb — 1.3.1

Forecast Combination Methods

forecastHybrid — 5.0.19

Convenient Functions for Ensemble Time Series Forecasts

forecastML — 0.9.0

Time Series Forecasting with Machine Learning Methods

forecTheta — 2.2

Forecasting Time Series by Theta Models

fpcb — 0.1.0

Predictive Confidence Bands for Functional Time Series Forecasting

fpp2 — 2.4

Data for "Forecasting: Principles and Practice" (2nd Edition)

fpp3 — 0.4.0

Data for "Forecasting: Principles and Practice" (3rd Edition)

fracdiff — 1.5-1

Fractionally Differenced ARIMA aka ARFIMA(P,d,q) Models

fredr — 2.1.0

An R Client for the 'FRED' API

freqdom.fda — 0.9.1

Functional Time Series: Dynamic Functional Principal Components

freqdom — 2.0.1

Frequency Domain Based Analysis: Dynamic PCA

fsMTS — 0.1.5

Feature Selection for Multivariate Time Series

fts —

R Interface to 'tslib' (a Time Series Library in C++)

ftsa — 6.1

Functional Time Series Analysis

funtimes — 8.1

Functions for Time Series Analysis

garma — 0.9.10

Fitting and Forecasting Gegenbauer ARMA Time Series Models

GAS — 0.3.3

Generalized Autoregressive Score Models

gdpc — 1.1.2

Generalized Dynamic Principal Components

ggdemetra — 0.2.2

'ggplot2' Extension for Seasonal and Trading Day Adjustment with 'RJDemetra'

ggseas — 0.5.4

'stats' for Seasonal Adjustment on the Fly with 'ggplot2'

glarma — 1.6-0

Generalized Linear Autoregressive Moving Average Models

GlarmaVarSel — 1.0

Variable Selection in Sparse GLARMA Models

GMDH — 1.6

Short Term Forecasting via GMDH-Type Neural Network Algorithms

gmvarkit — 2.0.2

Estimate Gaussian and Student's t Mixture Vector Autoregressive Models

GNAR — 1.1.1

Methods for Fitting Network Time Series Models

graphicalVAR — 0.3

Graphical VAR for Experience Sampling Data

gratis — 1.0.0

Generating Time Series with Diverse and Controllable Characteristics

gravitas — 0.1.3

Explore Probability Distributions for Bivariate Temporal Granularities

greybox — 1.0.2

Toolbox for Model Building and Forecasting

gsarima — 0.1-5

Two Functions for Generalized SARIMA Time Series Simulation

gsignal — 0.3-2

Signal Processing

gtop — 0.2.0

Game-Theoretically OPtimal (GTOP) Reconciliation Method

HarmonicRegression — 1.0

Harmonic Regression to One or more Time Series

HDTSA — 1.0.1

High Dimensional Time Series Analysis Tools

hht — 2.1.4

The Hilbert-Huang Transform: Tools and Methods

hts — 6.0.2

Hierarchical and Grouped Time Series

hwwntest — 1.3.1

Tests of White Noise using Wavelets

ifultools — 2.0-23

Insightful Research Tools

imputePSF — 0.1.0

Impute Missing Data in Time Series Data with PSF Based Method

imputeTestbench — 3.0.3

Test Bench for the Comparison of Imputation Methods

imputeTS — 3.2

Time Series Missing Value Imputation

IncDTW —

Incremental Calculation of Dynamic Time Warping

influxdbr — 0.14.2

R Interface to InfluxDB

InspectChangepoint — 1.1

High-Dimensional Changepoint Estimation via Sparse Projection

itsmr — 1.9

Time Series Analysis Using the Innovations Algorithm

KFAS — 1.4.6

Kalman Filter and Smoother for Exponential Family State Space Models

KFKSDS — 1.6

Kalman Filter, Smoother and Disturbance Smoother

kza —

Kolmogorov-Zurbenko Adaptive Filters

legion — 0.1.0

Forecasting Using Multivariate Models

locits — 1.7.3

Test of Stationarity and Localized Autocovariance

lomb — 2.0

Lomb-Scargle Periodogram

LongMemoryTS — 0.1.0

Long Memory Time Series

LPStimeSeries — 1.0-5

Learned Pattern Similarity and Representation for Time Series

LSTS — 2.1

Locally Stationary Time Series

ltsa — 1.4.6

Linear Time Series Analysis

lubridate — 1.8.0

Make Dealing with Dates a Little Easier

MAPA — 2.0.4

Multiple Aggregation Prediction Algorithm

mar1s — 2.1.1

Multiplicative AR(1) with Seasonal Processes

mAr — 1.1-2

Multivariate AutoRegressive analysis

MARSS — 3.11.4

Multivariate Autoregressive State-Space Modeling

mbsts — 2.2

Multivariate Bayesian Structural Time Series

mclcar — 0.2-0

Estimating Conditional Auto-Regressive (CAR) Models using Monte Carlo Likelihood Methods

Mcomp — 2.8

Data from the M-Competitions

meboot — 1.4-9.2

Maximum Entropy Bootstrap for Time Series

mfbvar — 0.5.6

Mixed-Frequency Bayesian VAR Models

mFilter — 0.1-5

Miscellaneous Time Series Filters

mgm — 1.2-12

Estimating Time-Varying k-Order Mixed Graphical Models

mixAR — 0.22.6

Mixture Autoregressive Models

mlVAR — 0.5

Multi-Level Vector Autoregression

modeltime — 1.1.1

The Tidymodels Extension for Time Series Modeling

modeltime.ensemble — 1.0.0

Ensemble Algorithms for Time Series Forecasting with Modeltime

mondate — 0.10.02

Keep Track of Dates in Terms of Months

mrf — 0.1.5

Multiresolution Forecasting

mssm — 0.1.5

Multivariate State Space Models

MSwM — 1.5

Fitting Markov Switching Models

MTS — 1.0.3

All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models

mtsdi — 0.3.5

Multivariate Time Series Data Imputation

multDM — 1.1.3

Multivariate Version of the Diebold-Mariano Test

MultipleBubbles — 0.2.0

Test and Detection of Explosive Behaviors for Time Series

multitaper — 1.0-15

Spectral Analysis Tools using the Multitaper Method

mvLSW — 1.2.4

Multivariate, Locally Stationary Wavelet Process Estimation

nardl — 0.1.6

Nonlinear Cointegrating Autoregressive Distributed Lag Model

nets — 0.9.1

Network Estimation for Time Series


Non-Gaussian State-Space with Exact Marginal Likelihood

NlinTS — 1.4.5

Models for Non Linear Causality Detection in Time Series

nlts — 1.0-2

Nonlinear Time Series Analysis

nnfor — 0.9.6

Time Series Forecasting with Neural Networks

nonlinearTseries — 0.2.11

Nonlinear Time Series Analysis

npst — 2.0

Generalization of Hewitt's Seasonality Test

nsarfima —

Methods for Fitting and Simulating Non-Stationary ARFIMA Models

NTS — 1.1.2

Nonlinear Time Series Analysis

odpc — 2.0.4

One-Sided Dynamic Principal Components

onlineVAR — 0.1-1

Online Fitting of Time-Adaptive Lasso Vector Auto Regression

opera — 1.2.0

Online Prediction by Expert Aggregation

otsad — 0.2.0

Online Time Series Anomaly Detectors

paleoTS — 0.5.2

Analyze Paleontological Time-Series

partsm — 1.1-3

Periodic Autoregressive Time Series Models

pastecs — 1.3.21

Package for Analysis of Space-Time Ecological Series

PCA4TS — 0.1

Segmenting Multiple Time Series by Contemporaneous Linear Transformation

pcdpca — 0.4

Dynamic Principal Components for Periodically Correlated Functional Time Series

pcts — 0.15.2

Periodically Correlated and Periodically Integrated Time Series

pdc — 1.0.3

Permutation Distribution Clustering

pdfetch — 0.2.5

Fetch Economic and Financial Time Series Data from Public Sources

perARMA — 1.6

Periodic Time Series Analysis

pomp — 4.1

Statistical Inference for Partially Observed Markov Processes

portes — 5.0

Portmanteau Tests for Univariate and Multivariate Time Series Models

ProbReco —

Score Optimal Probabilistic Forecast Reconciliation

profoc — 0.8.5

Probabilistic Forecast Combination Using CRPS Learning

prophet — 1.0

Automatic Forecasting Procedure

psd — 2.1.0

Adaptive, Sine-Multitaper Power Spectral Density and Cross Spectrum Estimation

PSF — 0.4

Forecasting of Univariate Time Series Using the Pattern Sequence-Based Forecasting (PSF) Algorithm

ptw — 1.9-16

Parametric Time Warping

Quandl — 2.11.0

API Wrapper for Quandl.com

quantspec — 1.2-3

Quantile-Based Spectral Analysis of Time Series

Rcatch22 — 0.1.13

Calculation of 22 CAnonical Time-Series CHaracteristics

rdbnomics — 0.6.4

Download DBnomics Data

readabs — 0.4.11

Download and Tidy Time Series Data from the Australian Bureau of Statistics

regspec — 2.4

Non-Parametric Bayesian Spectrum Estimation for Multirate Data


Tools to Generate Vector Time Series

rhosa — 0.2.0

Higher-Order Spectral Analysis

RJDemetra — 0.1.9

Interface to 'JDemetra+' Seasonal Adjustment Software

Rlgt — 0.1-3

Bayesian Exponential Smoothing Models with Trend Modifications

Rlibeemd — 1.4.2

Ensemble Empirical Mode Decomposition (EEMD) and Its Complete Variant (CEEMDAN)

rmaf — 3.0.1

Refined Moving Average Filter

RMAWGEN — 1.3.7

Multi-Site Auto-Regressive Weather GENerator

robets — 1.4

Forecasting Time Series with Robust Exponential Smoothing

robfilter — 4.1.2

Robust Time Series Filters

RobKF — 1.0.2

Innovative and/or Additive Outlier Robust Kalman Filtering

robustarima — 0.2.6

Robust ARIMA Modeling

roll — 1.1.6

Rolling and Expanding Statistics

RSEIS — 4.0-3

Seismic Time Series Analysis Tools

Rsfar — 0.0.1

Seasonal Functional Autoregressive Models

Rssa — 1.0.4

A Collection of Methods for Singular Spectrum Analysis

RTransferEntropy — 0.2.14

Measuring Information Flow Between Time Series with Shannon and Renyi Transfer Entropy

rts — 1.1-3

Raster Time Series Analysis

rucrdtw — 0.1.4

R Bindings for the UCR Suite

rugarch — 1.4-6

Univariate GARCH Models

runner — 0.4.1

Running Operations for Vectors

runstats — 1.1.0

Fast Computation of Running Statistics for Time Series

rwt — 1.0.0

Rice Wavelet Toolbox wrapper

sazedR — 2.0.2

Parameter-Free Domain-Agnostic Season Length Detection in Time Series

scoringRules — 1.0.1

Scoring Rules for Parametric and Simulated Distribution Forecasts

scoringutils —

Utilities for Scoring and Assessing Predictions

SDD — 1.2

Serial Dependence Diagrams

sde — 2.0.15

Simulation and Inference for Stochastic Differential Equations

seas — 0.5-2

Seasonal Analysis and Graphics, Especially for Climatology

season — 0.3.13

Seasonal Analysis of Health Data

seasonal — 1.8.4

R Interface to X-13-ARIMA-SEATS

seasonalview — 0.3

Graphical User Interface for Seasonal Adjustment

seer — 1.1.7

Feature-Based Forecast Model Selection

Sim.DiffProc — 4.8

Simulation of Diffusion Processes

sleekts — 1.0.2

4253H, Twice Smoothing

slider — 0.2.2

Sliding Window Functions

smooth — 3.1.4

Forecasting Using State Space Models

smoots — 1.1.3

Nonparametric Estimation of the Trend and Its Derivatives in TS

sparsevar — 0.1.0

Sparse VAR/VECM Models Estimation

spectral — 2.0

Common Methods of Spectral Data Analysis

spTimer — 3.3.1

Spatio-Temporal Bayesian Modelling

statespacer — 0.4.0

State Space Modelling in 'R'

STFTS — 0.1.0

Statistical Tests for Functional Time Series

stlplus — 0.5.1

Enhanced Seasonal Decomposition of Time Series by Loess

stochvol — 3.2.0

Efficient Bayesian Inference for Stochastic Volatility (SV) Models

stR — 0.4

STR Decomposition

strucchange — 1.5-2

Testing, Monitoring, and Dating Structural Changes

strucchangeRcpp — 1.5-3-1.0.4

Testing, Monitoring, and Dating Structural Changes: C++ Version

stsm — 1.9

Structural Time Series Models

sugrrants — 0.2.8

Supporting Graphs for Analysing Time Series

surveillance — 1.19.1

Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena

svars — 1.3.8

Data-Driven Identification of SVAR Models

sweep — 0.2.3

Tidy Tools for Forecasting

sym.arma — 1.0

Autoregressive and Moving Average Symmetric Models

synthesis — 1.2.3

Generate Synthetic Data from Statistical Models

tbrf — 0.1.5

Time-Based Rolling Functions

Tcomp — 1.0.1

Data from the 2010 Tourism Forecasting Competition

tempdisagg — 1.0

Methods for Temporal Disaggregation and Interpolation of Time Series

tensorTS — 0.1.2

Factor and Autoregressive Models for Tensor Time Series

testcorr — 0.2.0

Testing Zero Correlation

tfarima — 0.2.1

Transfer Function and ARIMA Models

tframe — 2015.12-1.1

Time Frame Coding Kernel

thief — 0.3

Temporal Hierarchical Forecasting

Tides — 2.1

Quasi-Periodic Time Series Characteristics

tiger —

TIme series of Grouped ERrors

timechange — 0.0.2

Efficient Changing of Date-Times

timeDate — 3043.102

Rmetrics - Chronological and Calendar Objects

TimeProjection — 0.2.0

Time Projections

timesboot — 1.0

Bootstrap computations for time series objects

timeSeries — 3062.100

Financial Time Series Objects (Rmetrics)

timeseriesdb — 0.4.1

Manage Time Series for Official Statistics with R and PostgreSQL

timetk — 2.7.0

A Tool Kit for Working with Time Series in R

timsac — 1.3.6

Time Series Analysis and Control Package

tis — 1.39

Time Indexes and Time Indexed Series

tpr — 0.3-2

Temporal Process Regression

trend — 1.1.4

Non-Parametric Trend Tests and Change-Point Detection

TSA — 1.3

Time Series Analysis

tsbox — 0.3.1

Class-Agnostic Time Series

tsBSS — 1.0.0

Blind Source Separation and Supervised Dimension Reduction for Time Series

TSclust — 1.3.1

Time Series Clustering Utilities

tscount — 1.4.3

Analysis of Count Time Series

tsdb — 1.0-0

Terribly-Simple Data Base for Time Series

TSdbi — 2017.4-1

Time Series Database Interface

tsdecomp — 0.2

Decomposition of Time Series Data

tsdisagg2 — 0.1.0

Time Series Disaggregation

TSdist — 3.7

Distance Measures for Time Series Data

tsDyn — 10-1.2

Nonlinear Time Series Models with Regime Switching

tseries — 0.10-49

Time Series Analysis and Computational Finance

TSEntropies — 0.9

Time Series Entropies

tseriesChaos — 0.1-13.1

Analysis of Nonlinear Time Series

tseriesEntropy — 0.6-0

Entropy Based Analysis and Tests for Time Series

tsfeatures — 1.0.2

Time Series Feature Extraction

tsfknn — 0.5.0

Time Series Forecasting Using Nearest Neighbors

tsibble — 1.1.1

Tidy Temporal Data Frames and Tools

tsibbledata — 0.4.0

Diverse Datasets for 'tsibble'

tsibbletalk — 0.1.0

Interactive Graphics for Tsibble Objects

tsintermittent — 1.9

Intermittent Time Series Forecasting

TSMining — 1.0

Mining Univariate and Multivariate Motifs in Time-Series Data

tsModel — 0.6

Time Series Modeling for Air Pollution and Health

tsoutliers — 0.6-8

Detection of Outliers in Time Series

tsPI — 1.0.3

Improved Prediction Intervals for ARIMA Processes and Structural Time Series

TSrepr — 1.1.0

Time Series Representations

tsrobprep — 0.3.1

Robust Preprocessing of Time Series Data

tssim — 0.1.7

Simulation of Daily and Monthly Time Series

TSstudio — 0.1.6

Functions for Time Series Analysis and Forecasting

TSTutorial — 1.2.3

Fitting and Predict Time Series Interactive Laboratory

tsutils — 0.9.2

Time Series Exploration, Modelling and Forecasting

tswge — 1.0.0

Applied Time Series Analysis

UComp — 2.2.2

Automatic Unobserved Components Models

ugatsdb — 0.2.2

Uganda Time Series Database API

uGMAR — 3.4.2

Estimate Univariate Gaussian and Student's t Mixture Autoregressive Models

urca — 1.3-0

Unit Root and Cointegration Tests for Time Series Data

uroot — 2.1-2

Unit Root Tests for Seasonal Time Series

VAR.etp — 0.7

VAR modelling: estimation, testing, and prediction

vars — 1.5-6

VAR Modelling

VARDetect — 0.1.5

Multiple Change Point Detection in Structural VAR Models

VARshrink — 0.3.1

Shrinkage Estimation Methods for Vector Autoregressive Models

VARsignR — 0.1.3

Sign Restrictions, Bayesian, Vector Autoregression Models

WaveletComp — 1.1

Computational Wavelet Analysis

wavelets — 0.3-0.2

Functions for Computing Wavelet Filters, Wavelet Transforms and Multiresolution Analyses

waveslim — 1.8.2

Basic Wavelet Routines for One-, Two-, and Three-Dimensional Signal Processing

wavethresh — 4.6.8

Wavelets Statistics and Transforms

wavScalogram — 1.1.1

Wavelet Scalogram Tools for Time Series Analysis

WeightedPortTest — 1.0

Weighted Portmanteau Tests for Time Series Goodness-of-fit

wktmo — 1.0.5

Converting Weekly Data to Monthly Data

wwntests — 1.0.1

Hypothesis Tests for Functional Time Series

x12 — 1.10.0

Interface to 'X12-ARIMA'/'X13-ARIMA-SEATS' and Structure for Batch Processing of Seasonal Adjustment

x13binary — 1.1.57-2

Provide the 'x13ashtml' Seasonal Adjustment Binary

xts — 0.12.1

eXtensible Time Series

yuima — 1.15.0

The YUIMA Project Package for SDEs

ZIM — 1.1.0

Zero-Inflated Models (ZIM) for Count Time Series with Excess Zeros

zoo — 1.8-9

S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations)

ZRA — 0.2

Dynamic Plots for Time Series Forecasting

Task view list