NetF: A Novel Set of Time Series Features

This is the original implementation of NetF for the paper "Novel Features for Time Series Analysis: A Complex Networks Approach" published in Data Mining and Knowledge Discovery.

Please cite our paper if you use the code:

Silva, V.F., Silva, M.E., Ribeiro, P., Silva, F. Novel features for time series analysis: a complex networks approach. Data Min Knowl Disc 36, 1062–1101 (2022). https://doi.org/10.1007/s10618-022-00826-3

Requirements

• R (>= 3.6.0)

Configurations

Data

• All the data sets can be found here and a subset in the folder Data.
  • M3_data: M3 competition data from R package Mcomp
  • production_Brazil: Production in Brazil data, the set of observations of 9 agriculture products in meso-regions of Brazil
  • real_ts: Selected benchmark empirical data sets, namely, the set of 8 selected data sets from UEA & UCR Time Series Classification Repository and the 18Pairs data set from R package TSclust
  • ts_models: Data Generating Processes (DGP), the set of 11 linear and nonlinear time series models
• All the univariate time series data sets from UEA & UCR Time Series Classification Repository can be found here. Can also be downloaded here.
  • Datasets are stored in .RData files and are in the following formats:
    • matrix of ts objects, ie. mts, for the ts_models dataset
    • list of ts objects, for the remaining datasets
• All the complex networks (Visibility Graphs and Quantiles Graphs) generated from the time series data sets can be found here.
  • The networks/graphs are in R igraph format and stored in .RData files.
• All the feature vectores (from NetF, tsfeatures, Rcatch22) can be found here.
• All the empirical and experimental results can be found here.

Source Files

• main : runs procedures for the experiments presented in paper
• libraries : contains all required packages for the procedures
• ts_mapping : contains the main function to run time series mapping algorithms
  • vg_algorithm : contains the Natural and Horizontal Visibility Graph algorithms
  • qg_algorithm : contains the Quantile Graph algorithm
• net_features : contains the network feature functions to create the NetF
• comp_clustering : runs the main procedures for PCA and clustering analysis
• func_clustering : contains the auxiliary functions for de PCA and clustering tasks
• min_max_norm : contains the auxiliary functions to compute the Min-Max normalization of a data frame
• The folder main_tsmodels contains the source files for the empirical evaluation of DGP data
  • simm_models : contains the functions to simulate Data Generation Processes
  • dgp : generates the specific Data Generation Processes to the paper
  • main_features_tsmodels : runs the main procedures for generating VGs and QGs from synthetic DGP time series set, and for computing the feature vectors: NetF, tsfeatures and catch22
  • emp_eval_tsmodels : runs the main procedures for the empirical evaluation of synthetic DGP
• The folder main_Brazil contains the source files for the experimental evaluation of Production in Brazil data
  • main_features_Brazil : runs the main procedures for generating VGs and QGs from Production time series set, and for computing the feature vectors: NetF, tsfeatures and catch22
  • exp_eval_Brazil : runs the main procedures for the experimental evaluation of Production Brazil
• The folder main_M3 contains the source files for the experimental evaluation of M3 competition data
  • main_features_M3 : runs the main procedures for generating VGs and QGs from M3 time series set, and for computing the feature vectors: NetF, tsfeatures and catch22
  • exp_eval_M3 : runs the main procedures for the experimental evaluation of M3 data
• The folder main_realts contains the source files for the experimental evaluation of benchmark empirical data
  • read_empirical : reads benchmark empirical data sets from UEA & UCR Time Series Classification Repository
  • main_features_realts : runs the main procedures for generating VGs and QGs from time series sets from UEA & UCR repository, and for computing the feature vectors: NetF, tsfeatures and catch22
  • exp_eval_realts : runs the main procedures for the experimental evaluation of UEA & UCR data sets
  • main_features_pairs : runs the main procedures for generating VGs and QGs from 18 Pairs time series sets, and for computing the feature vectors: NetF, tsfeatures and catch22
  • exp_eval_pairs : runs the main procedures for the experimental evaluation of 18 Pairs