Found 2887 packages in 0.01 seconds
Time Series, Analysis and Application
Accompanies the book Rainer Schlittgen and Cristina Sattarhoff (2020) < https://www.degruyter.com/view/title/575978> "Angewandte Zeitreihenanalyse mit R, 4. Auflage" . The package contains the time series and functions used therein. It was developed over many years teaching courses about time series analysis.
Probabilistic Time Series Forecasting
Probabilistic time series forecasting via Natural Gradient Boosting for Probabilistic Prediction.
Hydro-Meteorology Time-Series
Functions for the management and treatment of hydrology and meteorology time-series stored in a 'Sqlite' data base.
Univariate Time Series Forecasting
An engine for univariate time series forecasting using different regression models in an autoregressive way. The engine provides an uniform interface for applying the different models. Furthermore, it is extensible so that users can easily apply their own regression models to univariate time series forecasting and benefit from all the features of the engine, such as preprocessings or estimation of forecast accuracy.
Time Series Cointegrated System
A set of functions to implement Time Series Cointegrated System (TSCS) spatial interpolation and relevant data visualization.
Raster Time Series Visualization
A lightweight 'R' package to visualize large raster time series, building on a fast temporal interpolation core.
A Gui for Simulating Time Series
This gui shows realisations of times series, currently ARMA and GARCH processes. It might be helpful for teaching and studying.
Nonlinear Time Series Analysis
Functions for nonlinear time series analysis. This package permits the computation of the most-used nonlinear statistics/algorithms including generalized correlation dimension, information dimension, largest Lyapunov exponent, sample entropy and Recurrence Quantification Analysis (RQA), among others. Basic routines for surrogate data testing are also included. Part of this work was based on the book "Nonlinear time series analysis" by Holger Kantz and Thomas Schreiber (ISBN: 9780521529020).
Intermittent Time Series Forecasting
Time series methods for intermittent demand forecasting. Includes Croston's method and its variants (Moving Average, SBA), and the TSB method. Users can obtain optimal parameters on a variety of loss functions, or use fixed ones (Kourenztes (2014)
Analyzing Categorical Time Series
An implementation of several functions for feature extraction in categorical time series datasets. Specifically, some features related to marginal distributions and serial dependence patterns can be computed. These features can be used to feed clustering and classification algorithms for categorical time series, among others. The package also includes some interesting datasets containing biological sequences. Practitioners from a broad variety of fields could benefit from the general framework provided by 'ctsfeatures'.