Time series feature extraction for data mining using DWT and DFT

By Fabian Mörchen. 2003

182 citations recorded by the source corpus.

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Citations in the corpus

Forecasting Across Time Series Databases using Long Short-Term Memory Networks on Groups of Similar Series (2017, background): “In terms of feature extraction, there is a lot of work present in the literature investigating the prospects of using features of a time series as a data-mining tool for extracting useful patterns from time series (Nanopoulos et al., 2001; Wang et al., 2006; Fulcher and Jones, 2014; Mörchen, 2003).”
Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach (2017, background): “In terms of feature extraction, there is a lot of work present in the literature investigating the use of features of a time series as a data-mining tool for extracting useful patterns (Nanopoulos et al., 2001; Wang et al., 2006; Fulcher and Jones, 2014; Mörchen, 2003).”
Machine learning methods for wind turbine condition monitoring: A review (2019, methodology): “…transform time domain signals into frequency domain; examples include the largest coef ﬁ cients of the Discrete Fourier/Wavelet transform [51,55,56], Morlet Wavelet feature extraction [57] and Wigner-Ville distribution [58], SVD [59], cluster-based Wavelet feature extraction [60],…”
Highly Comparative Feature-Based Time-Series Classification (2014, methodology): “to represent and classify control chart patterns [10], M€ orchen used features derived from wavelet and Fourier transforms of a range of time-series data sets to classify them [11], Wang et al.”
A review on time series data mining (2011, methodology): “On the other hand, comparison between DFT and DWT can be found in Wu et al. (2000b) and Morchen (2003) and a combination use of Fourier and wavelet transforms are presented in Kawagoe and Ueda (2002).”
catch22: CAnonical Time-series CHaracteristics (2019, unknown): “hand has typically been done manually [28,23,22,30,1].”
Characteristic-Based Clustering for Time Series Data (2006, methodology): “In particular, clustering algorithms will profit from the data preprocessing (Mörchen, 2003).”