TSrepr isR package for fast time series representations and dimensionality reduction computations (i.e. time series feature extraction). Z-score normalisation, min-max normalisation, forecasting accuracy measures and other useful functions implemented in C++ (Rcpp) and R.TSrepr package is available onCRAN.

Installation

You can installTSrepr directly fromCRAN:

install.packages("TSrepr")

Or development version fromGitHub with:

# 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
• 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 - MSE, MAE, RMSE, MdAE, MAPE, sMAPE, MAAPE, MASE

Usage

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 timesdata_paa<-repr_paa(data_ts,q=12,func=mean)data_pla<-repr_pla(data_ts,times=55,return="both")# returns both extracted places and valuesdata_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 atpetolau.github.io/TSrepr-time-series-representations,
• Check my blog post about clustering time series representations atpetolau.github.io/TSrepr-clustering-time-series-representations,
• Blog post about usingFeaClip representation in multiple data streams clustering atpetolau.github.io/Multiple-data-streams-clustering-in-r.

Contact

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

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

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