tsgarch - Univariate GARCH Models

Multiple flavors of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model with a large choice of conditional distributions. Methods for specification, estimation, prediction, filtering, simulation, statistical testing and more. Represents a partial re-write and re-think of 'rugarch', making use of automatic differentiation for estimation.

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garchcpp

6.79 score 19 stars 1 dependents 36 scripts 476 downloads

tsdistributions - Location Scale Standardized Distributions

Location-Scale based distributions parameterized in terms of mean, standard deviation, skew and shape parameters and estimation using automatic differentiation. Distributions include the Normal, Student and GED as well as their skewed variants ('Fernandez and Steel'), the 'Johnson SU', and the Generalized Hyperbolic. Also included is the semi-parametric piece wise distribution ('spd') with Pareto tails and kernel interior.

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distributionsfinanceprobability-distributionprobability-distributionsstatistical-distributionstimeseriescpp

6.24 score 4 stars 3 dependents 24 scripts 921 downloads

tsissm - Linear Innovations State Space Unobserved Components Model

Unobserved components time series model using the linear innovations state space representation (single source of error) with choice of error distributions and option for dynamic variance. Methods for estimation using automatic differentiation, automatic model selection and ensembling, prediction, filtering, simulation and backtesting. Based on the model described in Hyndman et al (2012) <doi:10.1198/jasa.2011.tm09771>.

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forecastingtime-seriescpp

5.48 score 3 stars 5 scripts 301 downloads

tsmarch - Multivariate ARCH Models

Feasible Multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models including Dynamic Conditional Correlation (DCC), Copula GARCH and Generalized Orthogonal GARCH with Generalized Hyperbolic distribution. A review of some of these models can be found in Boudt, Galanos, Payseur and Zivot (2019) <doi:10.1016/bs.host.2019.01.001>.

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econometricsfinancegarchmultivariate-timeseriestime-seriesopenblascpp

5.47 score 11 stars 18 scripts 703 downloads

tsmethods - Time Series Methods

Generic methods for use in a time series probabilistic framework, allowing for a common calling convention across packages. Additional methods for time series prediction ensembles and probabilistic plotting of predictions is included. A more detailed description is available at <https://www.nopredict.com/packages/tsmethods> which shows the currently implemented methods in the 'tsmodels' framework.

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4.73 score 3 stars 6 dependents 3 scripts 524 downloads

tsaux - Time Series Forecasting Auxiliary Functions

A suite of auxiliary functions that enhance time series estimation and forecasting, including a robust anomaly detection routine based on Chen and Liu (1993) <doi:10.2307/2290724> (imported and wrapped from the 'tsoutliers' package), utilities for managing calendar and time conversions, performance metrics to assess both point forecasts and distributional predictions, advanced simulation by allowing the generation of time series components—such as trend, seasonal, ARMA, irregular, and anomalies—in a modular fashion based on the innovations form of the state space model and a number of transformation methods including Box-Cox, Logit, 'Softplus-Logit' and Sigmoid.

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4.48 score 1 stars 1 dependents 8 scripts 582 downloads

tstests - 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.

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forecastingstatistical-tests

4.40 score 5 stars 5 scripts 800 downloads