# TSrepr - R package for time series representations

**TSrepr** is **R** package for
fast time series representations and dimensionality reduction
computations. Z-score normalisation, min-max normalisation, forecasting
accuracy measures and other useful functions implemented in C++ (Rcpp)
and R. **TSrepr** package is available on
CRAN.

## Installation

You can install **TSrepr** directly from
CRAN:

Or development version from GitHub with:

## Overview

All type of time series representations methods are implemented, and these are so far:

- Nondata adaptive:
- PAA - Piecewise Aggregate Approximation (
`repr_paa`

) - DWT - Discrete Wavelet Transform (
`repr_dwt`

) - DFT - Discrete Fourier Transform (
`repr_dft`

) - DCT - Discrete Cosine Transform (
`repr_dct`

) - SMA - Simple Moving Average (
`repr_sma`

) - PIP - Perceptually Important Points (
`repr_pip`

)

- PAA - Piecewise Aggregate Approximation (
- Data adaptive:
- SAX - Symbolic Aggregate Approximation (
`repr_sax`

) - PLA - Piecewise Linear Approximation (
`repr_pla`

)

- SAX - Symbolic Aggregate Approximation (
- Model-based:
- Mean seasonal profile - Average seasonal profile, Median
seasonal profile, etc. (
`repr_seas_profile`

) - Model-based seasonal representations based on linear (additive)
model (LM, RLM, L1, GAM) (
`repr_lm`

,`repr_gam`

) - Exponential smoothing seasonal coefficients (
`repr_exp`

)

- Mean seasonal profile - Average seasonal profile, Median
seasonal profile, etc. (
- Data dictated:
- FeaClip - Feature extraction from clipping representation
(
`repr_feaclip`

,`clipping`

) - FeaTrend - Feature extraction from trending representation
(
`repr_featrend`

,`trending`

) - FeaClipTrend - Feature extraction from clipping and trending
representation (
`repr_feacliptrend`

)

- FeaClip - Feature extraction from clipping representation
(

Additional useful functions are implemented as:

- Windowing (
`repr_windowing`

) - applies above mentioned representations to every window of a time series - Matrix of representations (
`repr_matrix`

) - applies above mentioned representations to every row of a matrix of time series - Normalisation functions - z-score (
`norm_z`

), min-max (`norm_min_max`

) - Normalisation functions with output also of scaling parameters -
z-score (
`norm_z_list`

), min-max (`norm_min_max_list`

) - Denormalisation functions - z-score (
`denorm_z`

), min-max (`denorm_min_max`

) - Forecasting accuracy measures - MAE, RMSE, MdAE, MAPE, sMAPE, MASE

## Usage

## Contact

For any suggestions and comments write me an email at: tsreprpackage@gmail.com