An efficient aggregation method for the symbolic representation of temporal data
@article{Chen2022AnEA, title={An efficient aggregation method for the symbolic representation of temporal data}, author={Xinye Chen and Stefan G{\"u}ttel}, journal={ACM Transactions on Knowledge Discovery from Data (TKDD)}, year={2022} }
Symbolic representations are a useful tool for the dimension reduction of temporal data, allowing for the efficient storage of and information retrieval from time series. They can also enhance the training of machine learning algorithms on time series data through noise reduction and reduced sensitivity to hyperparameters. The adaptive Brownian bridge-based aggregation (ABBA) method is one such effective and robust symbolic representation, demonstrated to accurately capture important trends and…
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SHOWING 1-10 OF 62 REFERENCES
ABBA: adaptive Brownian bridge-based symbolic aggregation of time series
- Computer ScienceData Mining and Knowledge Discovery
- 2020
Comparisons with the SAX and 1d-SAX representations are included in the form of performance profiles, showing that ABBA is often able to better preserve the essential shape information of time series compared to other approaches, in particular when time warping measures are used.
Experiencing SAX: a novel symbolic representation of time series
- Computer ScienceData Mining and Knowledge Discovery
- 2007
The utility of the new symbolic representation of time series formed is demonstrated, which allows dimensionality/numerosity reduction, and it also allows distance measures to be defined on the symbolic approach that lower bound corresponding distance measuresdefined on the original series.
Symbolic Representation of Time Series: A Hierarchical Coclustering Formalization
- Computer ScienceAALTD@PKDD/ECML
- 2015
The main contribution of this article is to formalize SAXO as a hierarchical coclustering approach, which results in representations that drastically improve the compression of data.
A symbolic representation of time series, with implications for streaming algorithms
- Computer ScienceDMKD '03
- 2003
A new symbolic representation of time series is introduced that is unique in that it allows dimensionality/numerosity reduction, and it also allows distance measures to be defined on the symbolic approach that lower bound corresponding distance measuresdefined on the original series.
An improved symbolic aggregate approximation distance measure based on its statistical features
- Computer ScienceiiWAS
- 2016
This work provides comprehensive analysis for the proposed SAX_SD and confirms both the highest classification accuracy and the highest dimensionality reduction ratio on University of California, Riverside datasets in comparison to state of the art methods such as SAX.
TrSAX-An improved time series symbolic representation for classification.
- Computer ScienceISA transactions
- 2019
A Novel Symbolic Aggregate Approximation for Time Series
- Computer ScienceIMCOM
- 2019
This paper uses Piecewise Aggregate Approximation approach to reduce dimensionality and discretize the mean value of each segment by SAX, and proposes a modified distance measure by integrating the SAX distance with a weighted trend distance.
HOT aSAX: A Novel Adaptive Symbolic Representation for Time Series Discords Discovery
- Computer ScienceACIIDS
- 2010
This paper introduces a k-means based algorithm for symbolic representations of time series called adaptive Symbolic Aggregate approXimation (aSAX) and proposes HOT aSAX algorithm for time series discords discovery, which produces greater pruning power than the previous approach.
Multivariate Time Series Classification by Combining Trend-Based and Value-Based Approximations
- Computer ScienceICCSA
- 2012
A feature-based classification approach to classify real-world multivariate time series generated by drilling rig sensors in the oil and gas industry using a novel representation of time series which combines trend-based and value-based approximations.
Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases
- Computer ScienceKnowledge and Information Systems
- 2001
This work introduces a new dimensionality reduction technique which it is called Piecewise Aggregate Approximation (PAA), and theoretically and empirically compare it to the other techniques and demonstrate its superiority.