R time series packages not included in CRAN Task View: Time Series Analysis
AEDForecasting: Change Point Analysis in ARIMA Forecasting
ALFRED: Downloading Time Series from ALFRED Database for Various Vintages
ardl.nardl: Linear and Nonlinear Autoregressive Distributed Lag Models
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
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
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
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
CNLTtsa: Complex-Valued Wavelet Lifting for Univariate and Bivariate Time Series Analysis
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
cpss: Change-Point Detection by Sample-Splitting Methods
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
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
decomposedPSF: Time Series Prediction with PSF and Decomposition Methods (EMD and EEMD)
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
dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis
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
exdqlm: Extended Dynamic Quantile Linear Models
extremogram: Estimation of Extreme Value Dependence for Time Series Data
fabisearch: Change Point Detection in High-Dimensional Time Series Networks
far: Modelization for Functional AutoRegressive Processes
fastOnlineCpt: Online Multivariate Changepoint Detection
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
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
FuzzyStatProb: Fuzzy Stationary Probabilities from a Sequence of Observations of an Unknown Markov Chain
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
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
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
jenga: Fast Extrapolation of Time Features using K-Nearest Neighbors
lite: Likelihood-Based Inference for Time Series Extremes
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
legion: Forecasting Using Multivariate Models
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
mFLICA: Leadership-Inference Framework for Multivariate Time Series
midasml: Estimation and Prediction Methods for High-Dimensional Mixed Frequency Time Series Data
MisRepARMA: Misreported Time Series Analysis
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
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
onlineforecast: Forecast Modelling for Online Applications
ocd: High-Dimensional Multiscale Online Changepoint Detection
ocp: Bayesian Online Changepoint Detection
onlineBcp: Online Bayesian Methods for Change Point Analysis
partialAR: Partial Autoregression
partialCI: Partial Cointegration
peacots: Periodogram Peaks in Correlated Time Series
phase: Analyse Biological Time-Series Data
PHSMM: Penalised Maximum Likelihood Estimation for Hidden Semi-Markov Models
PieceExpIntensity: Bayesian Model to Find Changepoints Based on Rates and Count Data
PNAR: Poisson Network Autoregressive Models
popstudy: Applied Techniques to Demographic and Time Series Analysis
portvine: Vine Based (Un)Conditional Portfolio Risk Measure Estimation
prais: Prais-Winsten Estimator for AR(1) Serial Correlation
psdr: Use Time Series to Generate and Compare Power Spectral Density
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')
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
seasonal: R Interface to X-13-ARIMA-SEATS
seastests: Seasonality Tests
shrinkTVP: Efficient Bayesian Inference for Time-Varying Parameter Models with Shrinkage
simts: Time Series Analysis Tools
SLBDD: Statistical Learning for Big Dependent Data
slm: Stationary Linear Models
sovereign: State-Dependent Empirical Analysis
SparseTSCGM: Sparse Time Series Chain Graphical Models
spooky: Time Feature Extrapolation Using Spectral Analysis and Jack-Knife Resampling
ssaBSS: Stationary Subspace Analysis
starvars: Vector Logistic Smooth Transition Models Estimation and Prediction
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
SuperGauss: Superfast Likelihood Inference for Stationary Gaussian Time Series
svines: Stationary Vine Copula Models
TAR: Bayesian Modeling of Autoregressive Threshold Time Series Models
tetragon: Automatic Sequence Prediction by Expansion of the Distance Matrix
theft: Tools for Handling Extraction of Features from Time Series
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
ts.extend: Stationary Gaussian ARMA Processes and Other Time-Series Utilities
tsfgrnn: Time Series Forecasting Using GRNN
tsiR: An Implementation of the TSIR Model
TSPred: Functions for Benchmarking Time Series Prediction
tsSelect: Execution of Time Series Models
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
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
WASP: Wavelet System Prediction
WaveletArima: Wavelet-ARIMA Model for Time Series Forecasting
wbsts: added Multiple Change-Point Detection for Nonstationary Time Series