TSrepr v1.0.4


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Time Series Representations

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.

Functions in TSrepr

Name Description
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
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Type Package
Date 2020-03-25
License GPL-3 | file LICENSE
Encoding UTF-8
LazyData true
LinkingTo Rcpp
RoxygenNote 7.1.0
URL https://petolau.github.io/package/, https://github.com/PetoLau/TSrepr/
BugReports https://github.com/PetoLau/TSrepr/issues
VignetteBuilder knitr
NeedsCompilation yes
Packaged 2020-03-25 18:53:52 UTC; PeterLaurinec
Repository CRAN
Date/Publication 2020-03-25 19:20:02 UTC

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