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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```
# TSrepr
[](https://travis-ci.org/PetoLau/TSrepr)
[](https://cran.r-project.org/package=TSrepr)
[](https://cran.r-project.org/package=TSrepr)
[](https://cran.r-project.org/package=TSrepr)
<!-- [](https://www.rpackages.io/package/TSrepr) -->
[](https://codecov.io/github/PetoLau/TSrepr?branch=master)
[](https://doi.org/10.21105/joss.00577)
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.
## Installation
You can install **TSrepr** directly from [CRAN](https://CRAN.R-project.org/package=TSrepr):
```{r cran-installation, eval = FALSE}
install.packages("TSrepr")
```
Or development version from [GitHub](https://github.com/PetoLau/TSrepr) with:
```{r gh-installation, eval = FALSE}
# install.packages("devtools")
devtools::install_github("PetoLau/TSrepr")
```
## 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`)
* Data adaptive:
- SAX - Symbolic Aggregate Approximation (`repr_sax`)
- PLA - Piecewise Linear Approximation (`repr_pla`)
* 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`)
* 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`)
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
* List of representations (`repr_list`) - applies above mentioned representations to every member of a list of time series with different lengths
* Normalisation functions - z-score (`norm_z`), min-max (`norm_min_max`), arctan (`norm_atan`), Box-Cox (`norm_boxcox`), Yeo-Johnson (`norm_yj`)
* Normalisation functions with output also of scaling parameters - z-score (`norm_z_list`), min-max (`norm_min_max_list`)
* Normalisation functions with defined parameters - z-score (`norm_z_params`), min-max (`norm_min_max_params`)
* Denormalisation functions - z-score (`denorm_z`), min-max (`denorm_min_max`), arctan (`denorm_atan`), Box-Cox (`denorm_boxcox`), Yeo-Johnson (`denorm_yj`)
* Forecasting accuracy measures - MSE, MAE, RMSE, MdAE, MAPE, sMAPE, MAAPE, MASE
## Usage
```{r paa_vs_pla, fig.height=3.8, fig.width=8, warning=FALSE}
library(TSrepr)
library(ggplot2)
data_ts <- as.numeric(elec_load[5,]) # electricity load consumption data
# Comparison of PAA and PLA
# Dimensionality of the time series will be reduced 8 times
data_paa <- repr_paa(data_ts, q = 12, func = mean)
data_pla <- repr_pla(data_ts, times = 55, return = "both") # returns both extracted places and values
data_plot <- data.frame(value = c(data_ts, data_paa, data_pla$points),
time = c(1:length(data_ts), seq(6, length(data_ts), by = 12),
data_pla$places),
type = factor(c(rep("Original", length(data_ts)),
rep(c("PAA", "PLA"), each = 56))))
ggplot(data_plot, aes(time, value, color = type, size = type)) +
geom_line(alpha = 0.8) +
scale_size_manual(values = c(0.6, 0.8, 0.8)) +
theme_bw()
```
## For more information
- Check my blog post at [petolau.github.io/TSrepr-time-series-representations](https://petolau.github.io/TSrepr-time-series-representations/),
- Check my blog post about clustering time series representations at [petolau.github.io/TSrepr-clustering-time-series-representations](https://petolau.github.io/TSrepr-clustering-time-series-representations/),
- Blog post about using *FeaClip* representation in multiple data streams clustering at [petolau.github.io/Multiple-data-streams-clustering-in-r](https://petolau.github.io/Multiple-data-streams-clustering-in-r/).
## Contact
- For any suggestions and comments write me an email at: [[email protected]](mailto:[email protected]).
- For contribution options, check [CONTRIBUTING.md](https://github.com/PetoLau/TSrepr/blob/master/CONTRIBUTING.md) file, please.
## Citation
Cite the package as:
- Laurinec, (2018). TSrepr R package: Time Series Representations. Journal of Open Source Software, 3(23), 577, https://doi.org/10.21105/joss.00577