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Multivariate Time Series Data Imputation
This is an EM algorithm based method for imputation of missing values in multivariate normal time series. The imputation algorithm accounts for both spatial and temporal correlation structures. Temporal patterns can be modeled using an ARIMA(p,d,q), optionally with seasonal components, a non-parametric cubic spline or generalized additive models with exogenous covariates. This algorithm is specially tailored for climate data with missing measurements from several monitors along a given region.
Inferring Causal Effects using Bayesian Structural Time-Series Models
Implements a Bayesian approach to causal impact estimation in time
series, as described in Brodersen et al. (2015)
Time Value of Money, Time Series Analysis and Computational Finance
Package for time value of money calculation, time series analysis and computational finance.
Superfast Likelihood Inference for Stationary Gaussian Time Series
Likelihood evaluations for stationary Gaussian time series are typically obtained via the Durbin-Levinson algorithm, which scales as O(n^2) in the number of time series observations. This package provides a "superfast" O(n log^2 n) algorithm written in C++, crossing over with Durbin-Levinson around n = 300. Efficient implementations of the score and Hessian functions are also provided, leading to superfast versions of inference algorithms such as Newton-Raphson and Hamiltonian Monte Carlo. The C++ code provides a Toeplitz matrix class packaged as a header-only library, to simplify low-level usage in other packages and outside of R.
Two Functions for Generalized SARIMA Time Series Simulation
Write SARIMA models in (finite) AR representation and simulate
generalized multiplicative seasonal autoregressive moving average (time) series
with Normal / Gaussian, Poisson or negative binomial distribution.
The methodology of this method is described in Briet OJT, Amerasinghe PH, and
Vounatsou P (2013)
Methods for Handling and Analyzing Time Series of Satellite Images
Provides functions and methods for: splitting large raster objects
into smaller chunks, transferring images from a binary format into raster
layers, transferring raster layers into an 'RData' file, calculating the
maximum gap (amount of consecutive missing values) of a numeric vector,
and fitting harmonic regression models to periodic time series. The homoscedastic
harmonic regression model is based on G. Roerink, M. Menenti and W. Verhoef (2000)
Time Series Goodness of Fit and Forecast Evaluation Tests
Goodness of Fit and Forecast Evaluation Tests for timeseries models. Includes, among others, the Generalized Method of Moments (GMM) Orthogonality Test of Hansen (1982), the Nyblom (1989) parameter constancy test, the sign-bias test of Engle and Ng (1993), and a range of tests for value at risk and expected shortfall evaluation.
Time Series Regression
Time series regression. The dyn class interfaces ts, irts(), zoo() and zooreg() time series classes to lm(), glm(), loess(), quantreg::rq(), MASS::rlm(), MCMCpack::MCMCregress(), quantreg::rq(), randomForest::randomForest() and other regression functions allowing those functions to be used with time series including specifications that may contain lags, diffs and missing values.
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. It generates percentile, bias-corrected, and accelerated intervals and estimates partial autocorrelations using Durbin-Levinson. This package calculates the autocorrelation power spectrum, computes cross-correlations between two time series, computes bandwidth for any time series, and performs autocorrelation frequency analysis. It also calculates the periodicity of a time series.
'data.table' Time-Series
High-frequency time-series support via 'nanotime' and 'data.table'.