TSFEL: Time Series Feature Extraction Library

被引：260

作者：

Barandas, Marilia ^{[1
]}

Folgado, Duarte ^{[1
]}

Fernandes, Leticia ^{[1
]}

Santos, Sara ^{[1
]}

Abreu, Mariana ^{[1
]}

Bota, Patricia ^{[1
]}

Liu, Hui ^{[2
]}

Schultz, Tanja ^{[2
]}

Gamboa, Hugo ^{[1
,3
]}

机构：

[1] Assoc Fraunhofer Portugal Res, Rua Alfredo Allen 455-461, Porto, Portugal

[2] Univ Bremen, Cognit Syst Lab, Bremen, Germany

[3] Univ Nova Lisboa, Fac Ciencias & Tecnol, Dept Fis, Lab Instrumentacao Engn Biomed & Fis Radiacao LIB, P-2892516 Monte De Caparica, Caparica, Portugal

来源：

SOFTWAREX | 2020年 / 11卷

关键词：

Time series; Machine learning; Feature extraction; !text type='Python']Python[!/text; TRANSFORM; ENTROPY;

D O I：

10.1016/j.softx.2020.100456

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Time series feature extraction is one of the preliminary steps of conventional machine learning pipelines. Quite often, this process ends being a time consuming and complex task as data scien-tists must consider a combination between a multitude of domain knowledge factors and coding implementation. We present in this paper a Python package entitled Time Series Feature Extraction Library (TSFEL), which computes over 60 different features extracted across temporal, statistical and spectral domains. User customisation is achieved using either an online interface or a conventional Python package for more flexibility and integration into real deployment scenarios. TSFEL is designed to support the process of fast exploratory data analysis and feature extraction on time series with computational cost evaluation. (C) 2020 The Authors. Published by Elsevier B.V.

引用

页数：7

共 50 条

[31] Feature Extraction over Multiple Representations for Time Series Classification
Gay, Dominique
Guigoures, Romain
Boulle, Marc
Clerot, Fabrice
NEW FRONTIERS IN MINING COMPLEX PATTERNS, NFMCP 2013, 2014, 8399 : 18 - 34
[32] Classification System for Time Series Data Based on Feature Pattern Extraction
Sugimura, Hiroshi
Matsumoto, Kazunori
2011 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2011, : 1340 - 1345
[33] Automatic feature extraction and selection for classification of cyclical time series data
Schneider, Tizian
Helwig, Nikolai
Schuetze, Andreas
TM-TECHNISCHES MESSEN, 2017, 84 (03) : 198 - 206
[34] Hierarchical Time Series Feature Extraction for Power Consumption Anomaly Detection
Ouyang, Zhiyou
Sun, Xiaokui
Yue, Dong
ADVANCED COMPUTATIONAL METHODS IN ENERGY, POWER, ELECTRIC VEHICLES, AND THEIR INTEGRATION, LSMS 2017, PT 3, 2017, 763 : 267 - 275
[35] Feature Extraction of Time series data for Wind Speed Power generation
Khanna, Manju
Srinath, N. K.
Mendiratta, J. K.
2016 IEEE 6TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING (IACC), 2016, : 169 - 173
[36] Novel hidden feature extraction method for chaotic time series prediction
Lei, Miao
Peng, Yu
Peng, Xiyuan
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument, 2014, 35 (01): : 1 - 7
[37] Feature extraction from time-series data for process monitoring
Fujiwara, T
Nishitani, H
KAGAKU KOGAKU RONBUNSHU, 1996, 22 (05) : 1103 - 1110
[38] FRUITS: feature extraction using iterated sums for time series classification
Diehl, Joscha
Krieg, Richard
DATA MINING AND KNOWLEDGE DISCOVERY, 2024, 38 (06) : 4122 - 4156
[39] Under Sampling Adaboosting Shapelet Transformation for Time Series Feature Extraction
Joo, Yohan
Jeong, Jongpil
COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2019, PT VI: 19TH INTERNATIONAL CONFERENCE, SAINT PETERSBURG, RUSSIA, JULY 14, 2019, PROCEEDINGS, PART VI, 2019, 11624 : 69 - 80
[40] Unsupervised feature extraction from multivariate time series for outlier detection
Matsue, Kiyotaka
Sugiyama, Mahito
INTELLIGENT DATA ANALYSIS, 2022, 26 (06) : 1451 - 1467

← 1 2 3 4 5 →