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TSrepr (version 1.0.4)

Time Series Representations

Description

Methods for representations (i.e. dimensionality reduction, preprocessing, feature extraction) of time series to help more accurate and effective time series data mining. Non-data adaptive, data adaptive, model-based and data dictated (clipped) representation methods are implemented. Also min-max and z-score normalisations, and forecasting accuracy measures are implemented.

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install.packages('TSrepr')

Monthly Downloads

427

Version

1.0.4

License

GPL-3 | file LICENSE

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Maintainer

Peter Laurinec

Last Published

March 25th, 2020

Functions in TSrepr (1.0.4)

norm_min_max_list

Min-Max normalization list
mdae

MdAE
repr_dwt

DWT representation
mase

MASE
mape

MAPE
repr_featrend

FeaTrend representation of time series
repr_dft

DFT representation by FFT
repr_feacliptrend

FeaClipTrend representation of time series
norm_z_list

Z-score normalization list
norm_z

Z-score normalisation
repr_pip

PIP representation
norm_z_params

Z-score normalisation with parameters
repr_sax

SAX - Symbolic Aggregate Approximation
repr_matrix

Computation of matrix of representations from matrix of time series
norm_min_max_params

Min-Max normalisation with parameters
repr_paa

PAA - Piecewise Aggregate Approximation
mse

MSE
norm_min_max

Min-Max normalisation
trending

Creates bit-level (trending) representation from a vector
smape

sMAPE
repr_exp

Exponential smoothing seasonal coefficients as representation
repr_feaclip

FeaClip representation of time series
repr_pla

PLA representation
repr_windowing

Windowing of time series
repr_sma

Simple Moving Average representation
repr_lm

Regression coefficients from linear model as representation
rleC

RLE (Run Length Encoding) written in C++
repr_dct

DCT representation
rmse

RMSE
repr_gam

GAM regression coefficients as representation
repr_seas_profile

Mean seasonal profile of time series
elec_load

2 weeks of electricity load data from 50 consumers.
denorm_z

Z-score denormalisation
maape

MAAPE
coefComp

Functions for linear regression model coefficients extraction
fast_stat

Fast statistic functions (helpers)
mae

MAE
denorm_min_max

Min-Max denormalisation
TSrepr

TSrepr package
clipping

Creates bit-level (clipped representation) from a vector