Binary time series model
Forecasts can be combined using ForecastComb which supports many forecast combination methods including simple, geometric and regression-based combinations. Forecast evaluation is provided in the accuracy function from forecast.
Distributional forecast evaluation using scoring rules is available in scoringRules Tidy tools for forecasting are provided by sweep , converting objects produced in forecast to "tidy" data frames. Frequency analysis Spectral density estimation is provided by spectrum in the stats package, including the periodogram, smoothed periodogram and AR estimates.
Bayesian spectral inference is provided by bspec. The Lomb-Scargle periodogram for unevenly sampled time series is computed by lomb. The wavelets package includes computing wavelet filters, wavelet transforms and multiresolution analyses. Wavelet methods for time series analysis based on Percival and Walden are given in wmtsa.
WaveletComp provides some tools for wavelet-based analysis of univariate and bivariate time series including cross-wavelets, phase-difference and significance tests. Tests of white noise using wavelets are provided by hwwntest. Further wavelet methods can be found in the packages brainwaver , rwt , waveslim , wavethresh and mvcwt.
Harmonic regression using Fourier terms is implemented in HarmonicRegression. The forecast package also provides some simple harmonic regression facilities via the fourier function. Decomposition and Filtering Filters and smoothing: The robfilter package provides several robust time series filters, while mFilter includes miscellaneous time series filters useful for smoothing and extracting trend and cyclical components.
Seasonal decomposition is discussed below. Autoregressive-based decomposition is provided by ArDec. Singular Spectrum Analysis is implemented in Rssa and spectral. Additional tools, including ensemble EMD, are available in hht. An alternative implementation of ensemble EMD and its complete variant are available in Rlibeemd. Enhanced STL decomposition is available in stlplus. Seasonal analysis of health data including regression models, time-stratified case-crossover, plotting functions and residual checks.
Seasonal analysis and graphics, especially for climatology. Optimal deseasonalization for geophysical time series using AR fitting. Stationarity, Unit Roots, and Cointegration Stationarity and unit roots: MultipleBubbles tests for the existence of bubbles based on Phillips-Shi-Yu Time series costationarity determination is provided by costat.
LSTS has functions for locally stationary time series analysis. Locally stationary wavelet models for nonstationary time series are implemented in wavethresh including estimation, plotting, and simulation functionality for time-varying spectra. The Engle-Granger two-step method with the Phillips-Ouliaris cointegration test is implemented in tseries and urca. The latter additionally contains functionality for the Johansen trace and lambda-max tests.
CommonTrend provides tools to extract and plot common trends from a cointegration system. Parameter estimation and inference in a cointegrating regression are implemented in cointReg.
Nonlinear Time Series Analysis Nonlinear autoregression: Neural network autoregression is also provided in GMDH. Autoregression Markov switching models are provided in MSwM , while dependent mixtures of latent Markov models are given in depmix and depmixS4 for categorical and continuous time series. Various tests for nonlinearity are provided in fNonlinear. Additional functions for nonlinear time series are available in nlts and nonlinearTseries.
Fractal time series modeling and analysis is provided by fractal. Dynamic Regression Models Dynamic linear models: A convenient interface for fitting dynamic regression models via OLS is available in dynlm ; an enhanced approach that also works with other regression functions and more time series classes is implemented in dyn.
More advanced dynamic system equations can be fitted using dse. Functions for distributed lag non-linear modelling are provided in dlnm. Time-varying parameter models can be fitted using the tpr package. Dynamic modeling of various kinds is available in dynr including discrete and continuous time, linear and nonlinear models, and different types of latent variables.
These models are restricted to be stationary. Automated VAR models and networks are available in autovarCore. Another implementation with bootstrapped prediction intervals is given in VAR. EvalEst facilitates Monte Carlo experiments to evaluate the associated estimation methods. Vector error correction models are available via the urca , ecm , vars , tsDyn packages, including versions with structural constraints and thresholding. Time series component analysis: Time series factor analysis is provided in tsfa.
ForeCA implements forecastable component analysis by searching for the best linear transformations that make a multivariate time series as forecastable as possible. PCA4TS finds a linear transformation of a multivariate time series giving lower-dimensional subseries that are uncorrelated with each other.
One-sided dynamic principal components are computed in odpc. Frequency-domain-based dynamic PCA is implemented in freqdom. Multivariate state space models An implementation is provided by the KFAS package which provides a fast multivariate Kalman filter, smoother, simulation smoother and forecasting.
Yet another implementation is given in the dlm package which also contains tools for converting other multivariate models into state space form. All of these packages assume the observational and state error terms are uncorrelated. Partially-observed Markov processes are a generalization of the usual linear multivariate state space models, allowing non-Gaussian and nonlinear models. These are implemented in the pomp package. Multivariate stochastic volatility models using latent factors are provided by factorstochvol.
TSdist provides distance measures for time series data. TSrepr includes methods for representing time series using dimension reduction and feature extraction. Methods for plotting and forecasting collections of hierarchical and grouped time series are provided by hts.
An alternative approach to reconciling forecasts of hierarchical time series is provided by gtop. Continuous time models Continuous time autoregressive modelling is provided in cts , while carfima allows for continuous-time ARFIMA models.
DiffProc simulates and models stochastic differential equations. Simulation and inference for stochastic differential equations is provided by sde and yuima. The boot package provides function tsboot for time series bootstrapping, including block bootstrap with several variants. Maximum entropy bootstrap for time series is available in meboot. BootPR computes bias-corrected forecasting and bootstrap prediction intervals for autoregressive time series.
Data from Hyndman, Koehler, Ord and Snyder Forecasting with exponential smoothing are in the expsmooth package. Data from Hyndman and Athanasopoulos Forecasting: Data from the M-competition and M3-competition are provided in the Mcomp package. Data from the Quandl online portal to financial, economical and social datasets can be queried interactively using the Quandl package.
Data from the Datamarket online portal can be fetched using the rdatamarket package. Data from Switzerland via dataseries. BETS provides access to the most important economic time series in Brazil. Data from Shumway and Stoffer are in the astsa package. TSdbi provides a common interface to time series databases. AER and Ecdat both contain many data sets including time series data from many econometrics text books Miscellaneous dtw: Dynamic time warping algorithms for computing and plotting pairwise alignments between time series.
Bayesian Model Averaging to create probabilistic forecasts from ensemble forecasts and weather observations. Zentralblatt MATH identifier Time series, auto-correlation, regression, etc. Asymptotic properties of estimators. Keywords Time series categorical data nonstationary Markov chains asymptotic estimation theory. More by Heinz Kaufmann Search this author in: Google Scholar Project Euclid. Abstract Article info and citation First page Abstract For the analysis of nonstationary categorical time series, a parsimonious and flexible class of models is proposed.
Article information Source Ann. Dates First available in Project Euclid: Asymptotic properties of estimators Keywords Time series categorical data nonstationary Markov chains asymptotic estimation theory Citation Kaufmann, Heinz. Download Email Please enter a valid email address.