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tsrobprep (version 0.3.2)

Robust Preprocessing of Time Series Data

Description

Methods for handling the missing values outliers are introduced in this package. The recognized missing values and outliers are replaced using a model-based approach. The model may consist of both autoregressive components and external regressors. The methods work robust and efficient, and they are fully tunable. The primary motivation for writing the package was preprocessing of the energy systems data, e.g. power plant production time series, but the package could be used with any time series data. For details, see Narajewski et al. (2021) .

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Install

install.packages('tsrobprep')

Monthly Downloads

225

Version

0.3.2

License

MIT + file LICENSE

Maintainer

Micha<c5><82> Narajewski

Last Published

February 22nd, 2022

Functions in tsrobprep (0.3.2)

auto_data_cleaning

Perform automatic data cleaning of time series data
impute_modelled_data

Impute modelled missing time series data
detect_outliers

Detects unreliable outliers in univariate time series data based on model-based clustering
model_missing_data

Model missing time series data
robust_decompose

Robust time series seasonal decomposition
GBload

The electricity actual total load in Great Britain in year 2018