R time series packages not included in CRAN Task View: Time Series Analysis (at least when they were added to this list)
acfMPeriod: Robust Estimation of the ACF from the M-Periodogram
ADTSA: 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: Change Point Analysis in ARIMA Forecasting
ALFRED: Downloading Time Series from ALFRED Database for Various Vintages
apt: Asymmetric Price Transmission
anomaly: Detecting Anomalies in Data
AnomalyScore: Anomaly Scoring for Multivariate Time Series
ardl.nardl: Linear and Nonlinear Autoregressive Distributed Lag Models
AriGaMyANNSVR: Hybrid ARIMA-GARCH and Two Specially Designed ML-Based Models
arima2: Likelihood Based Inference for ARIMA Modeling
ARIMAANN: Time Series Forecasting using ARIMA-ANN Hybrid Model
ARMALSTM: Fitting of Hybrid ARMA-LSTM Models
artfima: ARTFIMA Model Estimation
ASV: Stochastic Volatility Models with or without Leverage
ATAforecasting: Automatic Time Series Analysis and Forecasting Using the Ata Method
aTSA: Alternative Time Series Analysis
audrex: Automatic Dynamic Regression using Extreme Gradient Boosting
AutoregressionMDE: Minimum Distance Estimation in Autoregressive Model
autostsm: Automatic Structural Time Series Models
autoTS: Automatic Model Selection and Prediction for Univariate Time Series
bayesdfa: Bayesian Dynamic Factor Analysis (DFA) with 'Stan'
bayesGARCH: Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations
BayesProject: Fast Projection Direction for Multivariate Changepoint Detection
BEKKs: Multivariate Conditional Volatility Modelling and Forecasting
betategarch: Simulation, Estimation and Forecasting of Beta-Skew-t-EGARCH Models
beyondWhittle: Bayesian Spectral Inference for Stationary Time Series
bifurcatingr: Bifurcating Autoregressive Models
bimets: Time Series and Econometric Modeling
BINCOR: Estimate the Correlation Between Two Irregular Time Series
BHSBVAR: Structural Bayesian Vector Autoregression Models
bmgarch: Bayesian Multivariate GARCH Models
bootCT: Bootstrapping the ARDL Tests for Cointegration
bootspecdens: Testing equality of spectral densities
breakpoint: An R Package for Multiple Break-Point Detection via the Cross-Entropy Method
BreakPoints: Identify Breakpoints in Series of Data
bsplinePsd: Bayesian Nonparametric Spectral Density Estimation Using B-Spline Priors
BSS: Brownian Semistationary Processes
bvarsv: Bayesian Analysis of a Vector Autoregressive Model with Stochastic Volatility and Time-Varying Parameters
bvhar: Bayesian Vector Heterogeneous Autoregressive Modeling
bwd: Backward Procedure for Change-Point Detection
CATkit: Chronomics Analysis Toolkit (CAT): Periodicity Analysis
CausalImpact: Inferring Causal Effects using Bayesian Structural Time-Series Models
changedetection: Nonparametric Change Detection in Multivariate Linear Relationships
changepoints: A Collection of Change-Point Detection Methods
changepointsHD: Change-Point Estimation for Expensive and High-Dimensional Models
changepointsVar: Change-Points Detections for Changes in Variance
ChangePointTaylor: Identify Changes in Mean
ChangepointTesting: Change Point Estimation for Clustered Signals
CHFF: Closest History Flow Field Forecasting for Bivariate Time Series
cleanTS: Testbench for Univariate Time Series Cleaning
CliftLRD: Complex-Valued Wavelet Lifting Estimators of the Hurst Exponent for Irregularly Sampled Time Series
ClusterVAR: Fitting Latent Class Vector-Autoregressive (VAR) Models
CNLTtsa: Complex-Valued Wavelet Lifting for Univariate and Bivariate Time Series Analysis
complex: Time Series Analysis and Forecasting Using Complex Variables
ConsReg: Fits Regression & ARMA Models Subject to Constraints to the Coefficient
Copula.Markov: Copula-Based Estimation and Statistical Process Control for Serially Correlated Time Series
corbouli: Corbae-Ouliaris Frequency Domain Filtering
costat: Time Series Costationarity Determination
cpss: Change-Point Detection by Sample-Splitting Methods
CptNonPar: Nonparametric Change Point Detection for Multivariate Time Series
crops: Changepoints for a Range of Penalties (CROPS)
cpop: Detection of Multiple Changes in Slope in Univariate Time-Series
crqa: Recurrence Quantification Analysis for Categorical and Continuous Time-Series
ctsem: Continuous Time Structural Equation Modelling
dbacf: Autocovariance Estimation via Difference-Based Methods
DBfit: A Double Bootstrap Method for Analyzing Linear Models with Autoregressive Errors
DCCA: Detrended Fluctuation and Detrended Cross-Correlation Analysis
DeCAFS: Detecting Changes in Autocorrelated and Fluctuating Signals
decp: Complete Change Point Analysis
decompDL: Decomposition Based Deep Learning Models for Time Series Forecasting
decomposedPSF: Time Series Prediction with PSF and Decomposition Methods (EMD and EEMD)
deFit: Fitting Differential Equations to Time Series Data
deseats: Data-Driven Locally Weighted Regression for Trend and Seasonality in TS
descomponer: Seasonal Adjustment by Frequency Analysis
desla: Desparsified Lasso Inference for Time Series
detectR: Change Point Detection
dfms: Dynamic Factor Models
dlm: Bayesian and Likelihood Analysis of Dynamic Linear Models
DLSSM: Dynamic Logistic State Space Prediction Model
dsem: Fit Dynamic Structural Equation Models
dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis
dynmix: Estimation of Dynamic Finite Mixtures
dymo: Dynamic Mode Decomposition for Multivariate Time Feature Prediction
dynr: Dynamic Models with Regime-Switching
dynsim: Dynamic Simulations of Autoregressive Relationships
eemdARIMA: EEMD Based Auto Regressive Integrated Moving Average Model
EEMDlstm: EEMD Based LSTM Model for Time Series Forecasting
EpiSignalDetection: Signal Detection Analysis
EvalEst: Dynamic Systems Estimation - Extensions
EVI: Epidemic Volatility Index as an Early-Warning Tool
evoTS: Analyses of Evolutionary Time-Series
exuber: Econometric Analysis of Explosive Time Series
exdqlm: Extended Dynamic Quantile Linear Models
EXPAR: Fitting of Exponential Autoregressive (EXPAR) Model
EXPARMA: Fitting of Exponential Autoregressive Moving Average (EXPARMA) Model
extremogram: Estimation of Extreme Value Dependence for Time Series Data
fabisearch: Change Point Detection in High-Dimensional Time Series Networks
fableCount: INGARCH and GLARMA Models for Count Time Series in Fable Framework
far: Modelization for Functional AutoRegressive Processes
fastOnlineCpt: Online Multivariate Changepoint Detection
fastTS: Fast Time Series Modeling with the Sparsity Ranked Lasso
fatBVARS: Bayesian VAR with Stochastic volatility and fat tails (not on CRAN)
FCVAR: Estimation and Inference for the Fractionally Cointegrated VAR
fHMM: Fitting Hidden Markov Models to Financial Data
finnts: Microsoft Finance Time Series Forecasting Framework
forecasteR: Time Series Forecast System -- a web application for displaying, analysing and forecasting univariate time series.
forecastSNSTS: Forecasting for Stationary and Non-Stationary Time Series
fpcb: Predictive Confidence Bands for Functional Time Series Forecasting
fracdist: Numerical CDFs for Fractional Unit Root and Cointegration Tests
fsMTS: Feature Selection for Multivariate Time Series
fUnitRoots: Rmetrics - Modelling Trends and Unit Roots
FuzzyStatProb: Fuzzy Stationary Probabilities from a Sequence of Observations of an Unknown Markov Chain
GARCHIto: Provides functions to estimate model parameters and forecast future volatilities using the Unified GARCH-Ito and Realized GARCH-Ito models
garchmodels: The 'Tidymodels' Extension for GARCH Models
GARCHSK: Estimating a GARCHSK Model and GJRSK Model (time-varying skewness and kurtosis)
garchx: Flexible and Robust GARCH-X Modelling
gasmodel: Generalized Autoregressive Score Models
GenHMM1d: Goodness-of-Fit for Univariate Hidden Markov Models
geovol: Geopolitical Volatility (GEOVOL) Modelling
gets: General-to-Specific (GETS) Modelling and Indicator Saturation Methods
GPoM: Generalized Polynomial Modelling
gratis: Generating Time Series with Diverse and Controllable Characteristics
harbinger: A Unified Time Series Event Detection Framework
Hassani.SACF: Computing Lower Bound of Ljung-Box Test
HDCD: High-Dimensional Changepoint Detection
hmix: Hidden Markov Model for Predicting Time Sequences with Mixture Sampling
HMMcopula: Markov Regime Switching Copula Models Estimation and Goodness-of-Fit
hydroGOF: Goodness-of-Fit Functions for Comparison of Simulated and Observed Hydrological Time Series
JFE: Tools for Analyzing Time Series Data of Just Finance and Econometrics
Largevars: Testing Large VARs for the Presence of Cointegration
longmemo: Statistics for Long-Memory Processes (Book Jan Beran), and Related Functionality
MSinference: Multiscale Inference for Nonparametric Time Trend(s)
MultiGlarmaVarSel: Variable Selection in Sparse Multivariate GLARMA Models
HBSTM: Hierarchical Bayesian Space-Time Models for Gaussian Space-Time Data
hdiVAR: Statistical Inference for Noisy Vector Autoregression
HDTSA: High Dimensional Time Series Analysis Tools
hmmr: "Mixture and Hidden Markov Models with R" Datasets and Example Code
hpfilter: The One- And Two-Sided Hodrick-Prescott Filter
hwwntest: Tests of White Noise using Wavelets
iAR: Irregularly Observed Autoregressive Models
ICSS: ICSS (Iterative Cumulative Sum of Squares) Algorithm by Inclan/Tiao (1994)
IDetect: Isolate-Detect Methodology for Multiple Change-Point Detection
iForecast: Machine Learning Time Series Forecasting
imputeFin: Imputation of Financial Time Series with Missing Values and/or Outliers
InterNL: Time Series Intervention Model Using Non-Linear Function
invgamstochvol: Obtains the Log Likelihood for an Inverse Gamma Stochastic Volatility Model
jenga: Fast Extrapolation of Time Features using K-Nearest Neighbors
lite: Likelihood-Based Inference for Time Series Extremes
LMest: Generalized Latent Markov Models. Latent Markov models for longitudinal continuous and categorical data.
LPM: Linear Parametric Models Applied to Hydrological Series
kalmanfilter: Kalman Filter
kimfilter: Kim Filter
knnp: Time Series Prediction using K-Nearest Neighbors Algorithm (Parallel)
knnwtsim: K Nearest Neighbor Forecasting with a Tailored Similarity Metric
kcpRS: Kernel Change Point Detection on the Running Statistics
LaMa: Fast Numerical Maximum Likelihood Estimation for Latent Markov Models
legion: Forecasting Using Multivariate Models
liftLRD: Wavelet Lifting Estimators of the Hurst Exponent for Regularly and Irregularly Sampled Time Series
longitudinal: Analysis of Multiple Time Course Data
LSVAR: Estimation of Low Rank Plus Sparse Structured Vector Auto-Regressive (VAR) Model
LSWPlib: Simulation and Spectral Estimation of Locally Stationary Wavelet Packet Processes
m5: 'M5 Forecasting' Challenges Data
marima: Multivariate ARIMA and ARIMA-X Analysis
MazamaTimeSeries: Core Functionality for Environmental Time Series
memochange: Testing for Structural Breaks under Long Memory and Testing for Changes in Persistence
MetaCycle: Evaluate Periodicity in Large Scale Data
mFLICA: Leadership-Inference Framework for Multivariate Time Series
micss: Modified Iterative Cumulative Sum of Squares Algorithm
midasml: Estimation and Prediction Methods for High-Dimensional Mixed Frequency Time Series Data
MisRepARMA: Misreported Time Series Analysis
MixedIndTests: Tests of Randomness and Tests of Independence
mlmts: Machine Learning Algorithms for Multivariate Time Series
mlrv: Long-Run Variance Estimation in Time Series Regression
modeltime.resample: Resampling Tools for Time Series Forecasting
modifiedmk: Modified Versions of Mann Kendall and Spearman's Rho Trend Tests
mosum: Moving Sum Based Procedures for Changes in the Mean
mrf: Multiresolution Forecasting
mssm: Multivariate State Space Models
multivar: Penalized Estimation and Forecasting of Multiple Subject Vector Autoregressive (multi-VAR) Models
mvDFA: Multivariate Detrended Fluctuation Analysis
mvgam: Multivariate (Dynamic) Generalized Additive Models
mvMonitoring: Multi-State Adaptive Dynamic Principal Component Analysis for Multivariate Process Monitoring
naive: Empirical Extrapolation of Time Feature Patterns
neverhpfilter: An Alternative to the Hodrick-Prescott Filter
ngboostForecast: Probabilistic Time Series Forecasting
NHMSAR: Non-Homogeneous Markov Switching Autoregressive Models
NonlinearTSA: Nonlinear Time Series Analysis
nortsTest: Assessing Normality of Stationary Process
nowcastDFM: Dynamic Factor Models (DFMs) for Nowcasting
npcp: Some Nonparametric CUSUM Tests for Change-Point Detection in Possibly Multivariate Observations
NVAR: Nonlinear Vector Autoregression Models
NVCSSL: Nonparametric Varying Coefficient Spike-and-Slab Lasso
onlineforecast: Forecast Modelling for Online Applications
ocd: High-Dimensional Multiscale Online Changepoint Detection
ocp: Bayesian Online Changepoint Detection
OLCPM: Online Change Point Detection for Matrix-Valued Time Series
onlineBcp: Online Bayesian Methods for Change Point Analysis
outliers.ts.oga: Efficient Outlier Detection in Heterogeneous Time Series Databases
partialAR: Partial Autoregression
partialCI: Partial Cointegration
patterncausality: 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: Threshold Model and Unit Root Tests in Cross-Section and Time Series Data
peacots: Periodogram Peaks in Correlated Time Series
perARMA: Periodic Time Series Analysis
phase: Analyse Biological Time-Series Data
PHSMM: Penalised Maximum Likelihood Estimation for Hidden Semi-Markov Models
PPMiss: Copula-Based Estimator for Long-Range Dependent Processes under Missing Data
PieceExpIntensity: Bayesian Model to Find Changepoints Based on Rates and Count Data
PNAR: Poisson Network Autoregressive Models
popbayes: Bayesian Model to Estimate Population Trends from Counts Series
popstudy: Applied Techniques to Demographic and Time Series Analysis
portes: Portmanteau Tests for Time Series Models
portvine: Vine Based (Un)Conditional Portfolio Risk Measure Estimation
prais: Prais-Winsten Estimator for AR(1) Serial Correlation
PRSim: Stochastic Simulation of Streamflow Time Series using Phase Randomization
psdr: Use Time Series to Generate and Compare Power Spectral Density
PWEV: PSO Based Weighted Ensemble Algorithm for Volatility Modelling
ragt2ridges: Ridge Estimation of Vector Auto-Regressive (VAR) Processes
RandomForestsGLS: Random Forests for Dependent Data
Rbeast: Bayesian Change-Point Detection and Time Series Decomposition
Rcatch22: Calculation of 22 CAnonical Time-Series CHaracteristics
RChest: Locating Distributional Changes in Highly Dependent Time Series
RecordTest: Inference Tools in Time Series Based on Record Statistics
rego: Automatic Time Series Forecasting and Missing Value Imputation
rEDM: Empirical Dynamic Modeling ('EDM')
rkt: Mann-Kendall Test, Seasonal and Regional Kendall Tests
robustarima: Robust ARIMA Modeling. Functions for fitting a linear regression model with ARIMA errors using a filtered tau-estimate.
rumidas: Univariate GARCH-MIDAS, Double-Asymmetric GARCH-MIDAS and MEM-MIDAS
rtrend: Trend Estimating Tools
santaR: Short Asynchronous Time-Series Analysis
sarima: Simulation and Prediction with Seasonal ARIMA Models
sdrt: Estimating the Sufficient Dimension Reduction Subspaces in Time Series
seasonal: R Interface to X-13-ARIMA-SEATS
seastests: Seasonality Tests
seqHMM: Mixture Hidden Markov Models for Social Sequence Data and Other Multivariate, Multichannel Categorical Time Series
setartree: A Novel and Accurate Tree Algorithm for Global Time Series Forecasting
shrinkTVP: Efficient Bayesian Inference for Time-Varying Parameter Models with Shrinkage
shrinkTVPVAR: 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
simts: Time Series Analysis Tools
SLBDD: Statistical Learning for Big Dependent Data
slm: Stationary Linear Models
SNSeg: Self-Normalization(SN) Based Change-Point Estimation for Time Series
sovereign: State-Dependent Empirical Analysis
SparseTSCGM: Sparse Time Series Chain Graphical Models
spectralAnomaly: Detect Anomalies Using the Spectral Residual Algorithm. Apply the spectral residual algorithm to data, such as a time series, to detect anomalies.
Spillover: Spillover/Connectedness Index Based on VAR Modelling
spooky: Time Feature Extrapolation Using Spectral Analysis and Jack-Knife Resampling
srlTS: Sparsity-Ranked Lasso for Time Series
ssaBSS: Stationary Subspace Analysis
sstvars: Toolkit for Reduced Form and Structural Smooth Transition Vector Autoregressive Models
starvars: Vector Logistic Smooth Transition Models Estimation and Prediction
stcpR6: Sequential Test and Change-Point Detection Algorithms Based on E-Values / E-Detectors
STFTS: Statistical Tests for Functional Time Series
stlARIMA: STL Decomposition and ARIMA Hybrid Forecasting Model
stlELM: Hybrid Forecasting Model Based on STL Decomposition and ELM
StVAR: Student's t Vector Autoregression (StVAR)
stepR: Multiscale Change-Point Inference
sufficientForecasting: Sufficient Forecasting using Factor Models
SuperGauss: Superfast Likelihood Inference for Stationary Gaussian Time Series
surveil: Time Series Models for Disease Surveillance
SVDNF: Discrete Nonlinear Filtering for Stochastic Volatility Models
svines: Stationary Vine Copula Models
TAR: Bayesian Modeling of Autoregressive Threshold Time Series Models
TCIU: 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
tdata: Prepare Your Time-Series Data for Further Analysis
tetragon: Automatic Sequence Prediction by Expansion of the Distance Matrix
theft: Tools for Handling Extraction of Features from Time Series
timeSeriesDataSets: Time Series Data Sets
TimeVizPro: Dynamic Data Explorer: Visualize and Forecast with 'TimeVizPro'
TrendLSW: Wavelet Methods for Analysing Locally Stationary Time Series
tsdataleaks: Exploit Data Leakages in Time Series Forecasting Competitions
tsmarch: Multivariate ARCH Models
TSEAL: 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.
tspredit: Time Series Prediction Integrated Tuning
trendsegmentR: Linear Trend Segmentation
TrendTM: Trend of High-Dimensional Time Series Matrix Estimation
TRMF: Temporally Regularized Matrix Factorization
TSANN: Time Series Artificial Neural Network
tsBSS: Blind Source Separation and Supervised Dimension Reduction for Time Series
tscopula: Time Series Copula Models
tseriesTARMA: Analysis of Nonlinear Time Series Through TARMA Models
ts.extend: Stationary Gaussian ARMA Processes and Other Time-Series Utilities
tsfgrnn: Time Series Forecasting Using GRNN
tsgc: Time Series Methods Based on Growth Curves
tsiR: An Implementation of the TSIR Model
TSLSTMplus: Long-Short Term Memory for Time-Series Forecasting, Enhanced
tsmethods: Time Series Methods -- generic methods for use in a time series probabilistic framework, allowing for a common calling convention across packages
TSPred: Functions for Benchmarking Time Series Prediction
tspredit: Time Series Prediction Integrated Tuning
tsSelect: Execution of Time Series Models
TSTutorial: Fitting and Predict Time Series Interactive Laboratory
tswge: Time Series for Data Science
tsxtreme: Bayesian Modelling of Extremal Dependence in Time Series
tvem: Time-Varying Effect Models
tvgarch: Time Varying GARCH Modelling
uGMAR: Estimate Univariate Gaussian or Student's t Mixture Autoregressive Model
UnitStat: Performs Unit Root Test Statistics
VARDetect: Multiple Change Point Detection in Structural VAR Models
VAR.spec: Allows Specifying a Bivariate VAR (Vector Autoregression) with Desired Spectral Characteristics
VARtests: Tests for Error Autocorrelation, ARCH Errors, and Cointegration in Vector Autoregressive Models
vccp: Vine Copula Change Point Detection in Multivariate Time Series
VLTimeCausality: Variable-Lag Time Series Causality Inference Framework
vse4ts: Identify Memory Patterns in Time Series Using Variance Scale Exponent
WASP: Wavelet System Prediction
WaveletArima: Wavelet-ARIMA Model for Time Series Forecasting
wbsts: added Multiple Change-Point Detection for Nonstationary Time Series
wwntests: Hypothesis Tests for Functional Time Series