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View Code? Open in Web Editor NEWA Python library for the fast symbolic approximation of time series
License: BSD 3-Clause "New" or "Revised" License
A Python library for the fast symbolic approximation of time series
License: BSD 3-Clause "New" or "Revised" License
@chenxinye Thanks for developing this great library. My questions are 1) Can this be applied to discontinuous time series where the sampling time / measurements is irregular?
Are there specific circumstances based on which it is better to apply the clustering based compression?
What is the key advantage of this method over SAX?
Thanks in advance.
I am consistently receiving a "This installation is not using Cython" and "cython fail" message when I use fABBA, even though I do have Cython installed. I have Cython version 3.0.8 and fABBA version 1.1.2, installed both using pip, and both packages appear in the same directory on my machine.
I'm not sure if this is affecting how fABBA actually runs, but as I follow the example code in the documentation, sometimes I get the same output, but sometimes not.
Joss's editorialbot there openjournals/joss-reviews#6294 found missing DOI's in the paper references:
Reference check summary (note 'MISSING' DOIs are suggestions that need verification):
OK DOIs
- None
MISSING DOIs
- 10.1007/s10618-020-00689-6 may be a valid DOI for title: ABBA: adaptive Brownian bridge-based symbolic aggregation of time series
- 10.1109/tit.1982.1056489 may be a valid DOI for title: Least squares quantization in PCM
- 10.1145/882082.882086 may be a valid DOI for title: A symbolic representation of time series, with implications for streaming algorithms
- 10.1145/3532622 may be a valid DOI for title: An efficient aggregation method for the symbolic representation of temporal data
- 10.1016/j.is.2023.102294 may be a valid DOI for title: ECG classification with learning ensemble based on symbolic discretization
- 10.1007/978-3-031-24378-3_4 may be a valid DOI for title: Fast Time Series Classification with Random Symbolic Subsequences
- 10.1007/s10618-007-0064-z may be a valid DOI for title: Experiencing SAX: a novel symbolic representation of time series
- 10.1109/seed55351.2022.00016 may be a valid DOI for title: Foreseer: Efficiently Forecasting Malware Event Series with Long Short-Term Memory
- 10.1016/j.ress.2023.109123 may be a valid DOI for title: Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load
- 10.1145/3448672 may be a valid DOI for title: A Framework for Generating Summaries from Temporal Personal Health Data
INVALID DOIs
- None
If you can check if the proposed DOI are right for your reference, and if so include them in your .bib that would be great :)
Hello! Thanks for developing this great library. i noticed that you also want to combine lstm in your subsequent work to predict time series. how is your current progress? after combining fabba with lstm by myself, the long-term prediction effect was always very poor, and it did not follow the trend for prediction.
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