Ordinal Time Series Analysis

An implementation of several functions for feature extraction in ordinal time series datasets. Specifically, some of the features proposed by Weiss (2019) can be computed. These features can be used to perform inferential tasks or to feed machine learning algorithms for ordinal time series, among others. The package also includes some interesting datasets containing financial time series. Practitioners from a broad variety of fields could benefit from the general framework provided by 'otsfeatures'.

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.

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.

Raster Time Series Visualization

A lightweight 'R' package to visualize large raster time series, building on a fast temporal interpolation core.

Detecting Nonstationarity in Time Series

Provides a nonvisual procedure for screening time series for nonstationarity in the context of intensive longitudinal designs, such as ecological momentary assessments. The method combines two diagnostics: one for detecting trends (based on the split R-hat statistic from Bayesian convergence diagnostics) and one for detecting changes in variance (a novel extension inspired by Levene's test). This approach allows researchers to efficiently and reproducibly detect violations of the stationarity assumption, especially when visual inspection of many individual time series is impractical. The procedure is suitable for use in all areas of research where time series analysis is central. For a detailed description of the method and its validation through simulations and empirical application, see Zitzmann, S., Lindner, C., Lohmann, J. F., & Hecht, M. (2024) "A Novel Nonvisual Procedure for Screening for Nonstationarity in Time Series as Obtained from Intensive Longitudinal Designs" < https://www.researchgate.net/publication/384354932_A_Novel_Nonvisual_Procedure_for_Screening_for_Nonstationarity_in_Time_Series_as_Obtained_from_Intensive_Longitudinal_Designs>.

Nonlinear Time Series Analysis

R functions for (non)linear time series analysis with an emphasis on nonparametric autoregression and order estimation, and tests for linearity / additivity.