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
distantia: Advanced Toolset for Efficient Time Series Dissimilarity Analysis
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
fDMA: Dynamic Model Averaging and Dynamic Model Selection for Continuous Outcomes
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
GreyModel: Fitting and Forecasting of Grey Model
Greymodels: Shiny App for Grey Forecasting Model
harbinger: A Unified Time Series Event Detection Framework
Hassani.SACF: Computing Lower Bound of Ljung-Box Test
HDCD: High-Dimensional Changepoint Detection
hdftsa: High-Dimensional Functional Time Series Analysis
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
IndGenErrors: 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: 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
MultiGrey: Fitting and Forecasting of Grey Model for Multivariate Time Series Data
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
qfa: Quantile-Frequency Analysis (QFA) of Time Series
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
rucm: Implementation of Unobserved Components Model (UCM)
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
sTSD: Simulate Time Series Diagnostics
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
utsf: Engine for Univariate Time Series Forecasting Using Different Regression Models in an Autoregressive Way
VARcpDetectOnline: Sequential Change Point Detection for High-Dimensional VAR Models
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