TSrepr R package: Time Series Representations

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Authors Peter Laurinec
Journal/Conference Name J. Open Source Software
Paper Category
Paper Abstract The TSrepr package contains various methods and types of time series representations including the Piecewise Aggregate Approximation (PAA), the Discrete Fourier Transform (DFT), the Perceptually Important Points (PIP), the Symbolic Aggregate approXimation (SAX), the Piecewise Linear Approximation (PLA) and Clipping. Except for these wellknown methods, additional methods suitable for seasonal time series are implemented. These methods are based on the model, for example multiple linear regression, robust regression, generalised additive model or triple exponential smoothing (Laurinec and Luck√° 2016, Laurinec et al. (2016)). Own developed feature extraction methods from the Clipping representation are also implemented FeaClip and FeaTrend. In Figure 1, the comparison of all eight available model-based representations in the TSrepr on electricity consumption time series from the randomly picked residential consumer is shown.
Date of publication 2018
Code Programming Language R

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