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Towards Understanding the Importance of Time-Series Features in Automated Algorithm Performance Prediction
25/25 checkable references clean · checked 2026-08-26
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
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The 25 checked references that resolve
resolves10.1007/s10618-016-0483-9
The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances
resolves10.1016/j.neucom.2018.03.067
Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh – A Python package)
resolves10.1007/s10618-020-00701-z
ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels
resolves10.1214/aos/1016218223
Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors)
resolves10.1098/rsif.2013.0048
Highly comparative time-series analysis: the empirical structure of time series and their methods
resolves10.1093/bib/bbp012
An introduction to artificial neural networks in bioinformatics--application to complex microarray and mass spectrometry datasets in cancer studies
resolves10.1016/j.ijforecast.2018.06.001
The M4 Competition: Results, findings, conclusion and way forward
resolves10.1016/j.ijforecast.2019.04.014
The M4 Competition: 100,000 time series and 61 forecasting methods
resolves10.1002/1099-131X(200011)19:6<515::AID-FOR754>3.0.CO;2-7
Evidence for the selection of forecasting methods
resolves10.2307/2344546
Experience with Forecasting Univariate Time Series and the Combination of Forecasts
resolves10.1007/s10618-020-00727-3
The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances
resolves10.1016/j.ijforecast.2019.03.017
A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting
The 20 references without a DOI — listed, not checked
no DOI — not checkedref5
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no DOI — not checkedref9
no DOI — not checkedMinirocket: A very fast (almost) deterministic transform for time series classification
no DOI — not checkedref12
no DOI — not checkedLess is more: Selecting the right benchmarking set of data for time series classification
no DOI — not checkedA study on ensemble learning for time series forecasting and the need for meta-learning
no DOI — not checkedref19
no DOI — not checkedA unified approach to interpreting model predictions
no DOI — not checkedVisualizing data using t-sne
no DOI — not checkedref29
no DOI — not checkedAn introduction to arma models
no DOI — not checkedScikit-learn: Machine learning in Python
no DOI — not checkedAn overview of the algorithm selection problem
no DOI — not checkedMetalearning how to forecast time series
no DOI — not checkedFformpp: Feature-based forecast model performance prediction
no DOI — not checkedAdvantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes
no DOI — not checkedref42
no DOI — not checkedMeta-learning
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