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Rmetrics - Autoregressive Conditional Heteroskedastic Modelling
Analyze and model heteroskedastic behavior in financial time series.
Measuring Information Flow Between Time Series with Shannon and Renyi Transfer Entropy
Measuring information flow between time series with Shannon and Rényi transfer entropy. See also Dimpfl and Peter (2013)
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)
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'.
Time Series Plot
A fast and elegant time series visualization package. In addition to the standard R plot types, this package supports candle sticks, open-high-low-close, and volume plots. Useful for visualizing any time series data, e.g., stock prices and technical indicators.