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

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

anomaly: Detecting Anomalies in Data

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

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

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

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

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

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

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

hydroGOF: Goodness-of-Fit Functions for Comparison of Simulated and Observed Hydrological Time Series

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

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

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

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

onlineBcp: Online Bayesian Methods for Change Point Analysis

partialAR: Partial Autoregression

partialCI: Partial Cointegration

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

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

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

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

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

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

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

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

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

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

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

wwntests: Hypothesis Tests for Functional Time Series