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https://github.com/beliavsky/r-time-series-task-view-supplement

R Time series packages not included in CRAN Task View: Time Series Analysis
https://github.com/beliavsky/r-time-series-task-view-supplement

arima arma-model autoregression cointegration cran econometrics forecast forecasting garch hmm nonlinear-time-series r r-package r-packages stochastic-volatility time-series time-series-analysis unit-root vector-autoregression volatility

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R Time series packages not included in CRAN Task View: Time Series Analysis

Awesome Lists containing this project

README 

          
R time series packages not included in [CRAN Task View: Time Series Analysis](https://cran.r-project.org/web/views/TimeSeries.html) (at least when they were added to this list)



[acfMPeriod](https://cran.r-project.org/web/packages/acfMPeriod/index.html): Robust Estimation of the ACF from the M-Periodogram



[ADTSA](https://cran.r-project.org/web/packages/ADTSA/index.html): Time Series Analysis. Analyzes autocorrelation and partial autocorrelation using surrogate methods and bootstrapping, and computes the acceleration constants for the vectorized moving block bootstrap provided by this package.



[AEDForecasting](https://cran.r-project.org/web/packages/AEDForecasting/index.html): Change Point Analysis in ARIMA Forecasting



[ALFRED](https://cran.r-project.org/web/packages/alfred/index.html): Downloading Time Series from ALFRED Database for Various Vintages



[apt](https://cran.r-project.org/web/packages/apt/index.html): Asymmetric Price Transmission



[anomaly](https://cran.r-project.org/web/packages/anomaly/index.html): Detecting Anomalies in Data



[AnomalyScore](https://cran.r-project.org/web/packages/AnomalyScore/index.html): Anomaly Scoring for Multivariate Time Series



[ardl.nardl](https://cran.r-project.org/web/packages/ardl.nardl/index.html): Linear and Nonlinear Autoregressive Distributed Lag Models



[AriGaMyANNSVR](https://cran.r-project.org/web/packages/AriGaMyANNSVR/index.html): Hybrid ARIMA-GARCH and Two Specially Designed ML-Based Models



[arima2](https://cran.r-project.org/web/packages/arima2/index.html): Likelihood Based Inference for ARIMA Modeling



[ARIMAANN](https://cran.r-project.org/web/packages/ARIMAANN/index.html): Time Series Forecasting using ARIMA-ANN Hybrid Model



[ARMALSTM](https://cran.r-project.org/web/packages/ARMALSTM/index.html): Fitting of Hybrid ARMA-LSTM Models



[artfima](https://cran.r-project.org/web/packages/artfima/index.html): ARTFIMA Model Estimation



[ASV](https://cran.r-project.org/web/packages/ASV/index.html): Stochastic Volatility Models with or without Leverage



[ATAforecasting](https://cran.r-project.org/web/packages/ATAforecasting/index.html): Automatic Time Series Analysis and Forecasting Using the Ata Method



[aTSA](https://cran.r-project.org/web/packages/aTSA/index.html): Alternative Time Series Analysis



[audrex](https://cran.r-project.org/web/packages/audrex/index.html): Automatic Dynamic Regression using Extreme Gradient Boosting



[AutoregressionMDE](https://cran.r-project.org/web/packages/AutoregressionMDE/index.html): Minimum Distance Estimation in Autoregressive Model



[autostsm](https://cran.r-project.org/web/packages/autostsm/index.html): Automatic Structural Time Series Models



[autoTS](https://cran.r-project.org/web/packages/autoTS/index.html): Automatic Model Selection and Prediction for Univariate Time Series



[BayesChange](https://cran.r-project.org/web/packages/BayesChange/index.html): Bayesian Methods for Change Points Analysis



[bayesdfa](https://cran.r-project.org/web/packages/bayesdfa/index.html): Bayesian Dynamic Factor Analysis (DFA) with 'Stan'



[bayesGARCH](https://cran.r-project.org/web/packages/bayesGARCH/index.html): Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations



[BayesProject](https://cran.r-project.org/web/packages/BayesProject/index.html): Fast Projection Direction for Multivariate Changepoint Detection



[bayesSSM](https://cran.r-project.org/web/packages/bayesSSM/index.html): Bayesian Methods for State Space Models



[BEKKs](https://cran.r-project.org/web/packages/BEKKs/index.html): Multivariate Conditional Volatility Modelling and Forecasting



[betategarch](https://cran.r-project.org/web/packages/betategarch/index.html): Simulation, Estimation and Forecasting of Beta-Skew-t-EGARCH Models



[beyondWhittle](https://cran.r-project.org/web/packages/beyondWhittle/index.html): Bayesian Spectral Inference for Stationary Time Series



[bifurcatingr](https://cran.r-project.org/web/packages/bifurcatingr/index.html): Bifurcating Autoregressive Models



[bimets](https://cran.r-project.org/web/packages/bimets/index.html): Time Series and Econometric Modeling



[BINCOR](https://cran.r-project.org/web/packages/BINCOR/index.html): Estimate the Correlation Between Two Irregular Time Series



[BHSBVAR](https://cran.r-project.org/web/packages/BHSBVAR/index.html): Structural Bayesian Vector Autoregression Models



[bmgarch](https://cran.r-project.org/web/packages/bmgarch/index.html): Bayesian Multivariate GARCH Models



[bootCT](https://cran.r-project.org/web/packages/bootCT/index.html): Bootstrapping the ARDL Tests for Cointegration



[bootspecdens](https://cran.r-project.org/web/packages/bootspecdens/index.html): Testing equality of spectral densities



[breakpoint](https://cran.r-project.org/web/packages/breakpoint/index.html): An R Package for Multiple Break-Point Detection via the Cross-Entropy Method



[BreakPoints](https://cran.r-project.org/web/packages/BreakPoints/index.html): Identify Breakpoints in Series of Data



[bsplinePsd](https://cran.r-project.org/web/packages/bsplinePsd/index.html): Bayesian Nonparametric Spectral Density Estimation Using B-Spline Priors



[BSS](https://cran.r-project.org/web/packages/BSS/index.html): Brownian Semistationary Processes



[BTSR](https://cran.r-project.org/web/packages/BTSR/index.html): Bounded Time Series Regression



[bvarsv](https://cran.r-project.org/web/packages/bvarsv/index.html): Bayesian Analysis of a Vector Autoregressive Model with Stochastic Volatility and Time-Varying Parameters



[bvhar](https://cran.r-project.org/web/packages/bvhar/index.html): Bayesian Vector Heterogeneous Autoregressive Modeling



[bwd](https://cran.r-project.org/web/packages/bwd/index.html): Backward Procedure for Change-Point Detection



[CATkit](https://cran.r-project.org/web/packages/CATkit/index.html): Chronomics Analysis Toolkit (CAT): Periodicity Analysis



[CausalImpact](https://cran.r-project.org/web/packages/CausalImpact/index.html): Inferring Causal Effects using Bayesian Structural Time-Series Models



[changedetection](https://cran.r-project.org/web/packages/changedetection/index.html): Nonparametric Change Detection in Multivariate Linear Relationships



[changepointGA](https://cran.r-project.org/web/packages/changepointGA/index.html): Changepoint Detection via Modified Genetic Algorithm



[changepoints](https://cran.r-project.org/web/packages/changepoints/index.html): A Collection of Change-Point Detection Methods



[changepointsHD](https://cran.r-project.org/web/packages/changepointsHD/index.html): Change-Point Estimation for Expensive and High-Dimensional Models



[changepointsVar](https://cran.r-project.org/web/packages/changepointsVar/index.html): Change-Points Detections for Changes in Variance



[ChangePointTaylor](https://cran.r-project.org/web/packages/ChangePointTaylor/index.html): Identify Changes in Mean



[ChangepointTesting](https://cran.r-project.org/web/packages/ChangepointTesting/index.html): Change Point Estimation for Clustered Signals



[CHFF](https://cran.r-project.org/web/packages/CHFF/index.html): Closest History Flow Field Forecasting for Bivariate Time Series



[cleanTS](https://cran.r-project.org/web/packages/cleanTS/index.html): Testbench for Univariate Time Series Cleaning



[CliftLRD](https://cran.r-project.org/web/packages/CliftLRD/index.html): Complex-Valued Wavelet Lifting Estimators of the Hurst Exponent for Irregularly Sampled Time Series



[ClusterVAR](https://cran.r-project.org/web/packages/ClusterVAR/index.html): Fitting Latent Class Vector-Autoregressive (VAR) Models



[CNLTtsa](https://cran.r-project.org/web/packages/CliftLRD/index.html): Complex-Valued Wavelet Lifting for Univariate and Bivariate Time Series Analysis



[complex](https://cran.r-project.org/web/packages/complex/index.html): Time Series Analysis and Forecasting Using Complex Variables



[ConsReg](https://cran.r-project.org/web/packages/ConsReg/index.html): Fits Regression & ARMA Models Subject to Constraints to the Coefficient



[Copula.Markov](https://cran.r-project.org/web/packages/Copula.Markov/index.html): Copula-Based Estimation and Statistical Process Control for Serially Correlated Time Series



[corbouli](https://cran.r-project.org/web/packages/corbouli/index.html): Corbae-Ouliaris Frequency Domain Filtering



[costat](https://cran.r-project.org/web/packages/costat/index.html): Time Series Costationarity Determination



[cpss](https://cran.r-project.org/web/packages/cpss/index.html): Change-Point Detection by Sample-Splitting Methods



[CptNonPar](https://cran.r-project.org/web/packages/CptNonPar/index.html): Nonparametric Change Point Detection for Multivariate Time Series



[crops](https://cran.r-project.org/web/packages/crops/index.html): Changepoints for a Range of Penalties (CROPS)



[cpop](https://cran.r-project.org/web/packages/cpop/index.html): Detection of Multiple Changes in Slope in Univariate Time-Series



[crqa](https://cran.r-project.org/web/packages/crqa/index.html): Recurrence Quantification Analysis for Categorical and Continuous Time-Series



[ctsem](https://cran.r-project.org/web/packages/ctsem/index.html): Continuous Time Structural Equation Modelling



[dbacf](https://cran.r-project.org/web/packages/dbacf/index.html): Autocovariance Estimation via Difference-Based Methods



[DBfit](https://cran.r-project.org/web/packages/DBfit): A Double Bootstrap Method for Analyzing Linear Models with Autoregressive Errors



[DCCA](https://cran.r-project.org/web/packages/DCCA/index.html): Detrended Fluctuation and Detrended Cross-Correlation Analysis



[DeCAFS](https://cran.r-project.org/web/packages/DeCAFS/index.html): Detecting Changes in Autocorrelated and Fluctuating Signals



[decp](https://cran.r-project.org/web/packages/decp/index.html): Complete Change Point Analysis



[decompDL](https://cran.r-project.org/web/packages/decompDL/index.html): Decomposition Based Deep Learning Models for Time Series Forecasting



[decomposedPSF](https://cran.r-project.org/web/packages/decomposedPSF/index.html): Time Series Prediction with PSF and Decomposition Methods (EMD and EEMD)



[deFit](https://cran.r-project.org/web/packages/deFit/index.html): Fitting Differential Equations to Time Series Data



[deseats](https://cran.r-project.org/web/packages/deseats/index.html): Data-Driven Locally Weighted Regression for Trend and Seasonality in TS



[descomponer](https://cran.r-project.org/web/packages/descomponer/index.html): Seasonal Adjustment by Frequency Analysis



[desla](https://cran.r-project.org/web/packages/desla/index.html): Desparsified Lasso Inference for Time Series



[detectR](https://cran.r-project.org/web/packages/detectR/index.html): Change Point Detection



[dfms](https://cran.r-project.org/web/packages/dfms/index.html): Dynamic Factor Models



[distantia](https://cran.r-project.org/web/packages/distantia/index.html): Advanced Toolset for Efficient Time Series Dissimilarity Analysis



[dlm](https://cran.r-project.org/web/packages/dlm/index.html): Bayesian and Likelihood Analysis of Dynamic Linear Models



[DLSSM](https://cran.r-project.org/web/packages/DLSSM/index.html): Dynamic Logistic State Space Prediction Model



[dma](https://cran.r-project.org/web/packages/dma/index.html): Dynamic model averaging for binary and continuous outcomes



[DREGAR](https://cran.r-project.org/web/packages/DREGAR/index.html): Regularized Estimation of Dynamic Linear Regression in the Presence of Autocorrelated Residuals



[dsem](https://cran.r-project.org/web/packages/dsem/index.html): Fit Dynamic Structural Equation Models



[dtwSat](https://cran.r-project.org/web/packages/dtwSat/index.html): Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis



[DWaveNARDL](https://cran.r-project.org/web/packages/DWaveNARDL/index.html): Dual Wavelet Based NARDL Model. The package uses the algorithm of the paper [A wavelet-based nonlinear ARDL model for assessing the exchange rate pass-through to crude oil prices](https://www.sciencedirect.com/science/article/abs/pii/S1042443114001437?via%3Dihub).



[dynmix](https://cran.r-project.org/web/packages/dynmix/index.html): Estimation of Dynamic Finite Mixtures



[dymo](https://cran.r-project.org/web/packages/dymo/index.html): Dynamic Mode Decomposition for Multivariate Time Feature Prediction



[dynr](https://cran.r-project.org/web/packages/dynr/index.html): Dynamic Models with Regime-Switching



[dynsim](https://cran.r-project.org/web/packages/dynsim/index.html): Dynamic Simulations of Autoregressive Relationships



[echos](https://cran.r-project.org/web/packages/echos/index.html): Echo State Networks for Time Series Modeling and Forecasting



[eemdARIMA](https://cran.r-project.org/web/packages/eemdARIMA/index.html): EEMD Based Auto Regressive Integrated Moving Average Model



[EEMDlstm](https://cran.r-project.org/web/packages/EEMDlstm/index.html): EEMD Based LSTM Model for Time Series Forecasting



[eNchange](https://cran.r-project.org/web/packages/eNchange/index.html): Ensemble Methods for Multiple Change-Point Detection



[EpiSignalDetection](https://cran.r-project.org/web/packages/EpiSignalDetection/index.html): Signal Detection Analysis



[EQRN](https://cran.r-project.org/web/packages/EQRN/index.html): Extreme Quantile Regression Neural Networks for Risk Forecasting



[EvalEst](https://cran.r-project.org/web/packages/EvalEst/index.html): Dynamic Systems Estimation - Extensions



[EVI](https://cran.r-project.org/web/packages/EVI/index.html): Epidemic Volatility Index as an Early-Warning Tool



[evoTS](https://cran.r-project.org/web/packages/evoTS/index.html): Analyses of Evolutionary Time-Series



[exuber](https://cran.r-project.org/web/packages/exuber/index.html): Econometric Analysis of Explosive Time Series



[exdqlm](https://cran.r-project.org/web/packages/exdqlm/index.html): Extended Dynamic Quantile Linear Models



[EXPAR](https://cran.r-project.org/web/packages/EXPAR/index.html): Fitting of Exponential Autoregressive (EXPAR) Model



[EXPARMA](https://cran.r-project.org/web/packages/EXPARMA/index.html): Fitting of Exponential Autoregressive Moving Average (EXPARMA) Model



[extremogram](https://cran.r-project.org/web/packages/extremogram/index.html): Estimation of Extreme Value Dependence for Time Series Data



[fabisearch](https://cran.r-project.org/web/packages/fabisearch/index.html): Change Point Detection in High-Dimensional Time Series Networks



[fableCount](https://cran.r-project.org/web/packages/fableCount/index.html): INGARCH and GLARMA Models for Count Time Series in Fable Framework



[far](https://cran.r-project.org/web/packages/far/index.html): Modelization for Functional AutoRegressive Processes



[fastOnlineCpt](https://cran.r-project.org/web/packages/fastOnlineCpt/index.html): Online Multivariate Changepoint Detection



[fastTS](https://cran.r-project.org/web/packages/fastTS/index.html): Fast Time Series Modeling with the Sparsity Ranked Lasso



[fatBVARS](https://github.com/hoanguc3m/fatBVARS): Bayesian VAR with Stochastic volatility and fat tails (not on CRAN)



[FCVAR](https://cran.r-project.org/web/packages/FCVAR/index.html): Estimation and Inference for the Fractionally Cointegrated VAR



[fDMA](https://cran.r-project.org/web/packages/fDMA/index.html): Dynamic Model Averaging and Dynamic Model Selection for Continuous Outcomes



[fEGarch](https://cran.r-project.org/web/packages/fEGarch/index.html): Estimation of a Broad Family of EGARCH Models (and other GARCH models)



[fHMM](https://cran.r-project.org/web/packages/fHMM/index.html): Fitting Hidden Markov Models to Financial Data



[finnts](https://cran.r-project.org/web/packages/finnts/index.html): Microsoft Finance Time Series Forecasting Framework



[FMM](https://cran.r-project.org/web/packages/FMM/index.html): Rhythmic Patterns Modeling by Frequency Modulated Moebius (FMM) Models



[FoCo2](https://cran.r-project.org/web/packages/FoCo2/index.html): Coherent Forecast Combination for Linearly Constrained Multiple Time Series



[forecasteR](https://cran.r-project.org/web/packages/forecasteR/index.html): Time Series Forecast System -- a web application for displaying, analysing and forecasting univariate time series.



[forecastSNSTS](https://cran.r-project.org/web/packages/forecastSNSTS/index.html): Forecasting for Stationary and Non-Stationary Time Series



[fpcb](https://cran.r-project.org/web/packages/fpcb/index.html): Predictive Confidence Bands for Functional Time Series Forecasting



[fracdist](https://cran.r-project.org/web/packages/fracdist/index.html): Numerical CDFs for Fractional Unit Root and Cointegration Tests



[fsMTS](https://cran.r-project.org/web/packages/fsMTS/index.html): Feature Selection for Multivariate Time Series



[fUnitRoots](https://cran.r-project.org/web/packages/fUnitRoots/index.html): Rmetrics - Modelling Trends and Unit Roots



[FuzzyStatProb](https://cran.r-project.org/web/packages/FuzzyStatProb/index.html): Fuzzy Stationary Probabilities from a Sequence of Observations of an Unknown Markov Chain



[GARCHIto](https://cran.r-project.org/web/packages/GARCHIto/index.html): Provides functions to estimate model parameters and forecast future volatilities using the [Unified GARCH-Ito](https://www.sciencedirect.com/science/article/abs/pii/S0304407616300914) and [Realized GARCH-Ito](https://www.sciencedirect.com/science/article/abs/pii/S0304407620301974) models



[garchmodels](https://cran.r-project.org/web/packages/garchmodels/index.html): The 'Tidymodels' Extension for GARCH Models



[GARCHSK](https://cran.r-project.org/web/packages/GARCHSK/index.html): Estimating a GARCHSK Model and GJRSK Model (time-varying skewness and kurtosis)



[garchx](https://cran.r-project.org/web/packages/garchx/index.html): Flexible and Robust GARCH-X Modelling



[GARCH.X](https://cran.r-project.org/web/packages/GARCH.X/index.html): Estimation and Exogenous Covariate Selection for GARCH-X Models



[gasmodel](https://cran.r-project.org/web/packages/gasmodel/index.html): Generalized Autoregressive Score Models 



[GenHMM1d](https://cran.r-project.org/web/packages/GenHMM1d/index.html): Goodness-of-Fit for Univariate Hidden Markov Models 



[geovol](https://cran.r-project.org/web/packages/geovol/index.html): Geopolitical Volatility (GEOVOL) Modelling



[gets](https://cran.r-project.org/web/packages/gets/index.html): General-to-Specific (GETS) Modelling and Indicator Saturation Methods



[GPoM](https://cran.r-project.org/web/packages/GPoM/index.html): Generalized Polynomial Modelling



[gratis](https://cran.r-project.org/web/packages/gratis/index.html): Generating Time Series with Diverse and Controllable Characteristics



[GreyModel](https://cran.r-project.org/web/packages/GreyModel/index.html): Fitting and Forecasting of Grey Model



[Greymodels](https://cran.r-project.org/web/packages/Greymodels/index.html): Shiny App for Grey Forecasting Model



[harbinger](https://cran.r-project.org/web/packages/harbinger/index.html): A Unified Time Series Event Detection Framework



[Hassani.SACF](https://cran.r-project.org/web/packages/Hassani.SACF/index.html): Computing Lower Bound of Ljung-Box Test



[HDCD](https://cran.r-project.org/web/packages/HDCD/index.html): High-Dimensional Changepoint Detection



[hdftsa](https://cran.r-project.org/web/packages/hdftsa/index.html): High-Dimensional Functional Time Series Analysis



[hdMTD](https://cran.r-project.org/web/packages/hdMTD/index.html): Inference for High-Dimensional Mixture Transition Distribution Models



[hmix](https://cran.r-project.org/web/packages/hmix/index.html): Hidden Markov Model for Predicting Time Sequences with Mixture Sampling



[HMMcopula](https://cran.r-project.org/web/packages/HMMcopula/index.html): Markov Regime Switching Copula Models Estimation and Goodness-of-Fit



[hmmTMB](https://cran.r-project.org/web/packages/hmmTMB/index.html): Fit Hidden Markov Models using Template Model Builder



[hydroGOF](https://cran.r-project.org/web/packages/hydroGOF/index.html): Goodness-of-Fit Functions for Comparison of Simulated and Observed Hydrological Time Series



[IndGenErrors](https://cran.r-project.org/web/packages/IndGenErrors/index.html): Tests of Independence Between Innovations of Generalized Error Models. Computation of test statistics of independence between (continuous) innovations of time series. They can be used with stochastic volatility models and Hidden Markov Models (HMM).



[JFE](https://cran.r-project.org/web/packages/JFE/index.html): Tools for Analyzing Time Series Data of Just Finance and Econometrics



[jumps](https://cran.r-project.org/web/packages/jumps/index.html): Hodrick-Prescott Filter with Jumps



[Largevars](https://cran.r-project.org/web/packages/Largevars/index.html): Testing Large VARs for the Presence of Cointegration



[longmemo](https://cran.r-project.org/web/packages/longmemo/index.html): Statistics for Long-Memory Processes (Book Jan Beran), and Related Functionality



[mantis](https://cran.r-project.org/web/packages/mantis/index.html): Multiple Time Series Scanner



[midasr](https://cran.r-project.org/web/packages/midasr/index.html): Mixed Data Sampling Regression



[MSinference](https://cran.r-project.org/web/packages/MSinference/index.html): Multiscale Inference for Nonparametric Time Trend(s)



[MultiGlarmaVarSel](https://cran.r-project.org/web/packages/MultiGlarmaVarSel/index.html): Variable Selection in Sparse Multivariate GLARMA Models



[HBSTM](https://cran.r-project.org/web/packages/HBSTM/index.html): Hierarchical Bayesian Space-Time Models for Gaussian Space-Time Data



[hdiVAR](https://cran.r-project.org/web/packages/hdiVAR/index.html): Statistical Inference for Noisy Vector Autoregression



[HDTSA](https://cran.r-project.org/web/packages/HDTSA/index.html): High Dimensional Time Series Analysis Tools



[hmmr](https://cran.r-project.org/web/packages/hmmr/index.html): "Mixture and Hidden Markov Models with R" Datasets and Example Code



[hpfilter](https://cran.r-project.org/web/packages/hpfilter/index.html): The One- And Two-Sided Hodrick-Prescott Filter



[hwwntest](https://cran.r-project.org/web/packages/hwwntest/index.html): Tests of White Noise using Wavelets



[iAR](https://cran.r-project.org/web/packages/iAR/index.html): Irregularly Observed Autoregressive Models



[ICSS](https://cran.r-project.org/web/packages/ICSS/index.html): ICSS (Iterative Cumulative Sum of Squares) Algorithm by Inclan/Tiao (1994)



[IDetect](https://cran.r-project.org/web/packages/IDetect/index.html): Isolate-Detect Methodology for Multiple Change-Point Detection



[iForecast](https://cran.r-project.org/web/packages/iForecast/index.html): Machine Learning Time Series Forecasting



[imputeFin](https://cran.r-project.org/web/packages/imputeFin/index.html): Imputation of Financial Time Series with Missing Values and/or Outliers



[InterNL](https://cran.r-project.org/web/packages/InterNL/index.html): Time Series Intervention Model Using Non-Linear Function



[invgamstochvol](https://cran.r-project.org/web/packages/invgamstochvol/index.html): Obtains the Log Likelihood for an Inverse Gamma Stochastic Volatility Model



[jenga](https://cran.r-project.org/web/packages/jenga/index.html): Fast Extrapolation of Time Features using K-Nearest Neighbors



[lite](https://cran.r-project.org/web/packages/lite/index.html): Likelihood-Based Inference for Time Series Extremes



[LMest](https://cran.r-project.org/web/packages/LMest/index.html): Generalized Latent Markov Models. Latent Markov models for longitudinal continuous and categorical data.



[LPM](https://cran.r-project.org/web/packages/LPM/index.html): Linear Parametric Models Applied to Hydrological Series



[kalmanfilter](https://cran.r-project.org/web/packages/kalmanfilter/index.html): Kalman Filter



[kimfilter](https://cran.r-project.org/web/packages/kimfilter/index.html): Kim Filter



[knnp](https://cran.r-project.org/web/packages/knnp/index.html): Time Series Prediction using K-Nearest Neighbors Algorithm (Parallel)



[knnwtsim](https://cran.r-project.org/web/packages/knnwtsim/index.html): K Nearest Neighbor Forecasting with a Tailored Similarity Metric



[kcpRS](https://cran.r-project.org/web/packages/kcpRS/index.html): Kernel Change Point Detection on the Running Statistics



[LaMa](https://cran.r-project.org/web/packages/LaMa/index.html): Fast Numerical Maximum Likelihood Estimation for Latent Markov Models



[legion](https://cran.r-project.org/web/packages/legion/index.html): Forecasting Using Multivariate Models



[liftLRD](https://cran.r-project.org/web/packages/liftLRD/index.html): Wavelet Lifting Estimators of the Hurst Exponent for Regularly and Irregularly Sampled Time Series



[longitudinal](https://cran.r-project.org/web/packages/longitudinal/index.html): Analysis of Multiple Time Course Data



[LSVAR](https://cran.r-project.org/web/packages/LSVAR/index.html): Estimation of Low Rank Plus Sparse Structured Vector Auto-Regressive (VAR) Model



[LSWPlib](https://cran.r-project.org/web/packages/LSWPlib/index.html): Simulation and Spectral Estimation of Locally Stationary Wavelet Packet Processes



[m5](https://cran.r-project.org/web/packages/m5/index.html): 'M5 Forecasting' Challenges Data



[marima](https://cran.r-project.org/web/packages/marima/index.html): Multivariate ARIMA and ARIMA-X Analysis



[MazamaTimeSeries](https://cran.r-project.org/web/packages/MazamaTimeSeries/index.html): Core Functionality for Environmental Time Series



[memochange](https://cran.r-project.org/web/packages/memochange/index.html): Testing for Structural Breaks under Long Memory and Testing for Changes in Persistence



[MetaCycle](https://cran.r-project.org/web/packages/MetaCycle/index.html): Evaluate Periodicity in Large Scale Data



[mFLICA](https://cran.r-project.org/web/packages/mFLICA/index.html): Leadership-Inference Framework for Multivariate Time Series



[micss](https://cran.r-project.org/web/packages/micss/index.html): Modified Iterative Cumulative Sum of Squares Algorithm



[midasml](https://cran.r-project.org/web/packages/midasml/index.html): Estimation and Prediction Methods for High-Dimensional Mixed Frequency Time Series Data



[MisRepARMA](https://cran.r-project.org/web/packages/MisRepARMA/index.html): Misreported Time Series Analysis



[MixedIndTests](https://cran.r-project.org/web/packages/MixedIndTests/index.html): Tests of Randomness and Tests of Independence



[mlmts](https://cran.r-project.org/web/packages/mlmts/index.html): Machine Learning Algorithms for Multivariate Time Series



[mlrv](https://cran.r-project.org/web/packages/mlrv/index.html): Long-Run Variance Estimation in Time Series Regression



[modeltime.resample](https://cran.r-project.org/web/packages/modeltime.resample/index.html): Resampling Tools for Time Series Forecasting



[modifiedmk](https://cran.r-project.org/web/packages/modifiedmk/index.html): Modified Versions of Mann Kendall and Spearman's Rho Trend Tests



[mosum](https://cran.r-project.org/web/packages/mosum/index.html): Moving Sum Based Procedures for Changes in the Mean



[mrf](https://cran.r-project.org/web/packages/mrf/index.html): Multiresolution Forecasting



[mssm](https://cran.r-project.org/web/packages/mssm/index.html): Multivariate State Space Models



[MSTest](https://cran.r-project.org/web/packages/MSTest/index.html): Hypothesis Testing for Markov Switching Models



[MultiGrey](https://cran.r-project.org/web/packages/MultiGrey/index.html): Fitting and Forecasting of Grey Model for Multivariate Time Series Data



[multivar](https://cran.r-project.org/web/packages/multivar/index.html): Penalized Estimation and Forecasting of Multiple Subject Vector Autoregressive (multi-VAR) Models



[mvDFA](https://cran.r-project.org/web/packages/mvDFA/index.html): Multivariate Detrended Fluctuation Analysis



[mvgam](https://cran.r-project.org/web/packages/mvgam/index.html): Multivariate (Dynamic) Generalized Additive Models



[mvMonitoring](https://cran.r-project.org/web/packages/mvMonitoring/index.html): Multi-State Adaptive Dynamic Principal Component Analysis for Multivariate Process Monitoring



[naive](https://cran.r-project.org/web/packages/naive/index.html): Empirical Extrapolation of Time Feature Patterns



[neverhpfilter](https://cran.r-project.org/web/packages/neverhpfilter/index.html): An Alternative to the Hodrick-Prescott Filter



[ngboostForecast](https://cran.r-project.org/web/packages/ngboostForecast/index.html): Probabilistic Time Series Forecasting



[NHMSAR](https://cran.r-project.org/web/packages/NHMSAR/index.html): Non-Homogeneous Markov Switching Autoregressive Models



[NonlinearTSA](https://cran.r-project.org/web/packages/NonlinearTSA/index.html): Nonlinear Time Series Analysis



[nortsTest](https://cran.r-project.org/web/packages/nortsTest/index.html): Assessing Normality of Stationary Process



[nowcastDFM](https://cran.r-project.org/web/packages/nowcastDFM/index.html): Dynamic Factor Models (DFMs) for Nowcasting



[npcp](https://cran.r-project.org/web/packages/npcp/index.html): Some Nonparametric CUSUM Tests for Change-Point Detection in Possibly Multivariate Observations



[NVAR](https://cran.r-project.org/web/packages/NVAR/index.html): Nonlinear Vector Autoregression Models



[NVCSSL](https://cran.r-project.org/web/packages/NVCSSL/index.html): Nonparametric Varying Coefficient Spike-and-Slab Lasso



[onlineforecast](https://cran.r-project.org/web/packages/onlineforecast/index.html): Forecast Modelling for Online Applications



[ocd](https://cran.r-project.org/web/packages/ocd/index.html): High-Dimensional Multiscale Online Changepoint Detection



[ocp](https://cran.r-project.org/web/packages/ocp/index.html): Bayesian Online Changepoint Detection



[OLCPM](https://cran.r-project.org/web/packages/OLCPM/index.html): Online Change Point Detection for Matrix-Valued Time Series



[onlineBcp](https://cran.r-project.org/web/packages/onlineBcp/index.html): Online Bayesian Methods for Change Point Analysis



[outliers.ts.oga](https://cran.r-project.org/web/packages/outliers.ts.oga/index.html): Efficient Outlier Detection in Heterogeneous Time Series Databases



[partialAR](https://cran.r-project.org/web/packages/partialAR/index.html): Partial Autoregression



[partialCI](https://cran.r-project.org/web/packages/partialCI/index.html): Partial Cointegration



[patterncausality](https://cran.r-project.org/web/packages/patterncausality/index.html): Pattern Causality Algorithm. The model proposes a robust methodology for detecting and reconstructing the hidden structure of dynamic complex systems through short-term forecasts and information embedded in reconstructed state spaces.



[pdR](https://cran.r-project.org/web/packages/pdR/index.html): Threshold Model and Unit Root Tests in Cross-Section and Time Series Data



[peacots](https://cran.r-project.org/web/packages/peacots/index.html): Periodogram Peaks in Correlated Time Series



[perARMA](https://cran.r-project.org/web/packages/perARMA/index.html): Periodic Time Series Analysis



[phase](https://cran.r-project.org/web/packages/phase/index.html): Analyse Biological Time-Series Data



[PHSMM](https://cran.r-project.org/web/packages/PHSMM/index.html): Penalised Maximum Likelihood Estimation for Hidden Semi-Markov Models



[PPMiss](https://cran.r-project.org/web/packages/PPMiss/index.html): Copula-Based Estimator for Long-Range Dependent Processes under Missing Data



[PieceExpIntensity](https://cran.r-project.org/web/packages/PieceExpIntensity/index.html): Bayesian Model to Find Changepoints Based on Rates and Count Data



[PNAR](https://cran.r-project.org/web/packages/PNAR/index.html): Poisson Network Autoregressive Models



[popbayes](https://cran.r-project.org/web/packages/popbayes/index.html): Bayesian Model to Estimate Population Trends from Counts Series



[popstudy](https://cran.r-project.org/web/packages/popstudy/index.html): Applied Techniques to Demographic and Time Series Analysis



[portes](https://cran.r-project.org/web/packages/portes/index.html): Portmanteau Tests for Time Series Models



[portvine](https://cran.r-project.org/web/packages/portvine/index.html): Vine Based (Un)Conditional Portfolio Risk Measure Estimation



[prais](https://cran.r-project.org/web/packages/prais/index.html): Prais-Winsten Estimator for AR(1) Serial Correlation



[PRSim](https://cran.r-project.org/web/packages/PRSim/index.html): Stochastic Simulation of Streamflow Time Series using Phase Randomization



[psdr](https://cran.r-project.org/web/packages/psdr/index.html): Use Time Series to Generate and Compare Power Spectral Density



[PWEV](https://cran.r-project.org/web/packages/PWEV/index.html): PSO Based Weighted Ensemble Algorithm for Volatility Modelling



[qfa](https://cran.r-project.org/web/packages/qfa/index.html): Quantile-Frequency Analysis (QFA) of Time Series



[quadVAR](https://cran.r-project.org/web/packages/quadVAR/index.html): Quadratic Vector Autoregression



[ragt2ridges](https://cran.r-project.org/web/packages/ragt2ridges/index.html): Ridge Estimation of Vector Auto-Regressive (VAR) Processes



[RandomForestsGLS](https://cran.r-project.org/web/packages/RandomForestsGLS/index.html): Random Forests for Dependent Data



[Rbeast](https://cran.r-project.org/web/packages/Rbeast/index.html): Bayesian Change-Point Detection and Time Series Decomposition



[Rcatch22](https://cran.r-project.org/web/packages/Rcatch22/index.html): Calculation of 22 CAnonical Time-Series CHaracteristics



[RChest](https://cran.r-project.org/web/packages/RChest/index.html): Locating Distributional Changes in Highly Dependent Time Series



[RecordTest](https://cran.r-project.org/web/packages/RecordTest/index.html): Inference Tools in Time Series Based on Record Statistics



[rego](https://cran.r-project.org/web/packages/rego/index.html): Automatic Time Series Forecasting and Missing Value Imputation



[rEDM](https://cran.r-project.org/web/packages/rEDM/index.html): Empirical Dynamic Modeling ('EDM')



[rkt](https://cran.r-project.org/web/packages/rkt/index.html): Mann-Kendall Test, Seasonal and Regional Kendall Tests



[robustarima](https://cran.r-project.org/web/packages/robustarima/index.html): Robust ARIMA Modeling. Functions for fitting a linear regression model with ARIMA errors using a filtered tau-estimate.



[rumidas](https://cran.r-project.org/web/packages/rumidas/index.html): Univariate GARCH-MIDAS, Double-Asymmetric GARCH-MIDAS and MEM-MIDAS



[rtrend](https://cran.r-project.org/web/packages/rtrend/index.html): Trend Estimating Tools



[rucm](https://cran.r-project.org/web/packages/rucm/index.html): Implementation of Unobserved Components Model (UCM)



[santaR](https://cran.r-project.org/web/packages/santaR/index.html): Short Asynchronous Time-Series Analysis



[sarima](https://cran.r-project.org/web/packages/sarima/index.html): Simulation and Prediction with Seasonal ARIMA Models



[sdrt](https://cran.r-project.org/web/packages/sdrt/index.html): Estimating the Sufficient Dimension Reduction Subspaces in Time Series



[seasonal](https://cran.r-project.org/web/packages/seasonal/index.html): R Interface to X-13-ARIMA-SEATS



[seastests](https://cran.r-project.org/web/packages/seastests/index.html): Seasonality Tests



[seqHMM](https://cran.r-project.org/web/packages/seqHMM/index.html): Mixture Hidden Markov Models for Social Sequence Data and Other Multivariate, Multichannel Categorical Time Series



[setartree](https://cran.r-project.org/web/packages/setartree/index.html): A Novel and Accurate Tree Algorithm for Global Time Series Forecasting



[shrinkTVP](https://cran.r-project.org/web/packages/shrinkTVP/index.html): Efficient Bayesian Inference for Time-Varying Parameter Models with Shrinkage



[shrinkTVPVAR](https://cran.r-project.org/web/packages/shrinkTVPVAR/index.html): Efficient Bayesian Inference for TVP-VAR-SV Models with Shrinkage. An associated paper is [Triple the Gamma—A Unifying Shrinkage Prior for Variance and Variable Selection in Sparse State Space and TVP Models](https://www.mdpi.com/2225-1146/8/2/20)



[simStateSpace](https://cran.r-project.org/web/packages/simStateSpace/index.html): Simulate Data from State Space Models



[simts](https://cran.r-project.org/web/packages/simts/index.html): Time Series Analysis Tools



[SLBDD](https://cran.r-project.org/web/packages/SLBDD/index.html): Statistical Learning for Big Dependent Data



[slm](https://cran.r-project.org/web/packages/slm/index.html): Stationary Linear Models



[SNSeg](https://cran.r-project.org/web/packages/SNSeg/index.html): Self-Normalization(SN) Based Change-Point Estimation for Time Series



[sovereign](https://cran.r-project.org/web/packages/sovereign/index.html): State-Dependent Empirical Analysis



[SparseTSCGM](https://cran.r-project.org/web/packages/SparseTSCGM/index.html): Sparse Time Series Chain Graphical Models



[spectralAnomaly](https://cran.r-project.org/web/packages/spectralAnomaly/index.html): Detect Anomalies Using the Spectral Residual Algorithm. Apply the spectral residual algorithm to data, such as a time series, to detect anomalies.



[Spillover](https://cran.r-project.org/web/packages/Spillover/index.html): Spillover/Connectedness Index Based on VAR Modelling



[spooky](https://cran.r-project.org/web/packages/spooky/index.html): Time Feature Extrapolation Using Spectral Analysis and Jack-Knife Resampling



[srlTS](https://cran.r-project.org/web/packages/srlTS/index.html): Sparsity-Ranked Lasso for Time Series



[ssaBSS](https://cran.r-project.org/web/packages/ssaBSS/index.html): Stationary Subspace Analysis



[sstvars](https://cran.r-project.org/web/packages/sstvars/index.html): Toolkit for Reduced Form and Structural Smooth Transition Vector Autoregressive Models



[starvars](https://cran.r-project.org/web/packages/starvars/index.html): Vector Logistic Smooth Transition Models Estimation and Prediction



[statioVAR](https://cran.r-project.org/web/packages/statioVAR/index.html): Trend Removal for Vector Autoregressive Workflows



[stcpR6](https://cran.r-project.org/web/packages/stcpR6/index.html): Sequential Test and Change-Point Detection Algorithms Based on E-Values / E-Detectors



[STFTS](https://cran.r-project.org/web/packages/STFTS/index.html): Statistical Tests for Functional Time Series



[stlARIMA](https://cran.r-project.org/web/packages/stlARIMA/index.html): STL Decomposition and ARIMA Hybrid Forecasting Model



[stlELM](https://cran.r-project.org/web/packages/stlELM/index.html): Hybrid Forecasting Model Based on STL Decomposition and ELM



[sTSD](https://cran.r-project.org/web/packages/sTSD/index.html): Simulate Time Series Diagnostics



[StVAR](https://cran.r-project.org/web/packages/StVAR/index.html): Student's t Vector Autoregression (StVAR)



[stepR](https://cran.r-project.org/web/packages/stepR/index.html): Multiscale Change-Point Inference



[sufficientForecasting](https://cran.r-project.org/web/packages/sufficientForecasting/index.html): Sufficient Forecasting using Factor Models



[SuperGauss](https://cran.r-project.org/web/packages/SuperGauss/index.html): Superfast Likelihood Inference for Stationary Gaussian Time Series



[surveil](https://cran.r-project.org/web/packages/surveil/index.html): Time Series Models for Disease Surveillance



[SVDNF](https://cran.r-project.org/web/packages/SVDNF/index.html): Discrete Nonlinear Filtering for Stochastic Volatility Models



[svines](https://cran.r-project.org/web/packages/svines/index.html): Stationary Vine Copula Models



[TAR](https://cran.r-project.org/web/packages/TAR/index.html): Bayesian Modeling of Autoregressive Threshold Time Series Models



[TCIU](https://cran.r-project.org/web/packages/TCIU/index.html): Spacekime Analytics, Time Complexity and Inferential Uncertainty. Provide the core functionality to transform longitudinal data to complex-time (kime) data using analytic and numerical techniques, visualize the original time-series and reconstructed kime-surfaces, perform model based (e.g., tensor-linear regression) and model-free classification and clustering methods in the book Dinov, ID and Velev, MV. (2021) [Data Science: Time Complexity, Inferential Uncertainty, and Spacekime Analytics](https://www.degruyter.com/document/doi/10.1515/9783110697827/html)



[tdata](https://cran.r-project.org/web/packages/tdata/index.html): Prepare Your Time-Series Data for Further Analysis



[tetragon](https://cran.r-project.org/web/packages/tetragon/index.html): Automatic Sequence Prediction by Expansion of the Distance Matrix



[theft](https://cran.r-project.org/web/packages/theft/index.html): Tools for Handling Extraction of Features from Time Series



[timeSeriesDataSets](https://cran.r-project.org/web/packages/timeSeriesDataSets/index.html): Time Series Data Sets



[timetools](https://cran.r-project.org/web/packages/timetools/index.html): Seasonal/Sequential (Instants/Durations, Even or not) Time Series



[TimeVizPro](https://cran.r-project.org/web/packages/TimeVizPro/index.html): Dynamic Data Explorer: Visualize and Forecast with 'TimeVizPro'



[TrendLSW](https://cran.r-project.org/web/packages/TrendLSW/index.html): Wavelet Methods for Analysing Locally Stationary Time Series



[trendsegmentR](https://cran.r-project.org/web/packages/trendsegmentR/index.html): Linear Trend Segmentation



[TrendTM](https://cran.r-project.org/web/packages/TrendTM/index.html): Trend of High-Dimensional Time Series Matrix Estimation



[TRMF](https://cran.r-project.org/web/packages/TRMF/index.html): Temporally Regularized Matrix Factorization



[tsdataleaks](https://cran.r-project.org/web/packages/tsdataleaks/index.html): Exploit Data Leakages in Time Series Forecasting Competitions



[tsmarch](https://cran.r-project.org/web/packages/tsmarch/index.html): Multivariate ARCH Models



[TSEAL](https://cran.r-project.org/web/packages/TSEAL/index.html): Time Series Analysis Library: allows one to perform a multivariate time series classification based on the use of Discrete Wavelet Transform for feature extraction, a step wise discriminant to select the most relevant features and finally, the use of a linear or quadratic discriminant for classification. 



[TSANN](https://cran.r-project.org/web/packages/TSANN/index.html): Time Series Artificial Neural Network



[tsBSS](https://cran.r-project.org/web/packages/tsBSS/index.html): Blind Source Separation and Supervised Dimension Reduction for Time Series



[tscopula](https://cran.r-project.org/web/packages/tscopula/index.html): Time Series Copula Models



[tseriesTARMA](https://cran.r-project.org/web/packages/tseriesTARMA/index.html): Analysis of Nonlinear Time Series Through TARMA Models



[ts.extend](https://cran.r-project.org/web/packages/ts.extend/index.html): Stationary Gaussian ARMA Processes and Other Time-Series Utilities



[tsfgrnn](https://cran.r-project.org/web/packages/tsfgrnn/index.html): Time Series Forecasting Using GRNN



[tsgc](https://cran.r-project.org/web/packages/tsgc/index.html): Time Series Methods Based on Growth Curves



[tsiR](https://cran.r-project.org/web/packages/tsiR/index.html): An Implementation of the TSIR Model



[TSLSTMplus](https://cran.r-project.org/web/packages/TSLSTMplus/index.html): Long-Short Term Memory for Time-Series Forecasting, Enhanced



[tsmethods](https://cran.r-project.org/web/packages/tsmethods/index.html): Time Series Methods -- generic methods for use in a time series probabilistic framework, allowing for a common calling convention across packages



[TSPred](https://cran.r-project.org/web/packages/TSPred/index.html): Functions for Benchmarking Time Series Prediction



[tspredit](https://cran.r-project.org/web/packages/tspredit/index.html): Time Series Prediction Integrated Tuning



[tsSelect](https://cran.r-project.org/web/packages/tsSelect/index.html): Execution of Time Series Models



[TSTutorial](https://cran.r-project.org/web/packages/TSTutorial/index.html): Fitting and Predict Time Series Interactive Laboratory



[tswge](https://cran.r-project.org/web/packages/tswge/index.html): Time Series for Data Science



[tsxtreme](https://cran.r-project.org/web/packages/tsxtreme/index.html): Bayesian Modelling of Extremal Dependence in Time Series



[tvem](https://cran.r-project.org/web/packages/tvem/index.html): Time-Varying Effect Models



[tvgarch](https://cran.r-project.org/web/packages/tvgarch/index.html): Time Varying GARCH Modelling



[tvGarchKF](https://cran.r-project.org/web/packages/tvGarchKF/index.html): Time-Varying Garch Models Through a State-Space Representation



[uGMAR](https://cran.r-project.org/web/packages/uGMAR/index.html): Estimate Univariate Gaussian or Student's t Mixture Autoregressive Model



[UnitStat](https://cran.r-project.org/web/packages/UnitStat/index.html): Performs Unit Root Test Statistics



[utsf](https://cran.r-project.org/web/packages/utsf/index.html): Engine for Univariate Time Series Forecasting Using Different Regression Models in an Autoregressive Way



[VARcpDetectOnline](https://cran.r-project.org/web/packages/VARcpDetectOnline/index.html): Sequential Change Point Detection for High-Dimensional VAR Models



[VARDetect](https://cran.r-project.org/web/packages/VARDetect/index.html): Multiple Change Point Detection in Structural VAR Models



[VAR.spec](https://cran.r-project.org/web/packages/VAR.spec/index.html): Allows Specifying a Bivariate VAR (Vector Autoregression) with Desired Spectral Characteristics



[VARtests](https://cran.r-project.org/web/packages/VARtests/index.html): Tests for Error Autocorrelation, ARCH Errors, and Cointegration in Vector Autoregressive Models



[vccp](https://cran.r-project.org/web/packages/vccp/index.html): Vine Copula Change Point Detection in Multivariate Time Series



[VGAMextra](https://cran.r-project.org/web/packages/VGAMextra/index.html): Additions and Extensions of the 'VGAM' Package. Comprises new family functions (ffs) to estimate several time series models by maximum likelihood using Fisher scoring, unlike popular packages in CRAN relying on optim(), including ARMA-GARCH-like models, the Order-(p, d, q) ARIMAX model (non- seasonal), the Order-(p) VAR model, error correction models for cointegrated time series, and ARMA-structures with Student-t errors. 



[VLTimeCausality](https://cran.r-project.org/web/packages/VLTimeCausality/index.html): Variable-Lag Time Series Causality Inference Framework



[vse4ts](https://cran.r-project.org/web/packages/vse4ts/index.html): Identify Memory Patterns in Time Series Using Variance Scale Exponent



[WASP](https://cran.r-project.org/web/packages/WASP/index.html): Wavelet System Prediction



[WaveletArima](https://cran.r-project.org/web/packages/WaveletArima/index.html): Wavelet-ARIMA Model for Time Series Forecasting



[wbsts](https://cran.r-project.org/web/packages/wbsts/index.html): added Multiple Change-Point Detection for Nonstationary Time Series



[wqc](https://cran.r-project.org/web/packages/wqc/index.html): Wavelet Quantile Correlation Analysis



[wwntests](https://cran.r-project.org/web/packages/wwntests/index.html): Hypothesis Tests for Functional Time Series



[xpect](https://cran.r-project.org/web/packages/xpect/index.html): Probabilistic Time Series Forecasting with XGBoost and Conformal Inference