Conference Information

LearnTeD 2023: Learning from Temporal Data

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Submission Date:
2023-05-22
Notification Date:
2023-07-17
Conference Date:
2023-10-09
Location:
Thessaloniki, Greece
Viewed: 7889   Tracked: 0   Attend: 0

Conference Partner Index (CP-I)

42.1 / 100
Ranked #4,153 of 5,687 conferences · Top 74%
Limited evidence: this conference is not listed in CCF / ICORE / QUALIS and has no acceptance-rate data on file, so most of the score falls back to the neutral baseline.
Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%) No data - scored at the neutral baseline of 50 —
Community attention (10%)
8
Public record completeness (15%)
25

Inputs used: Researchers who opened this page in the past 24 months: 2

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 25% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-28

Call For Papers

LearnTeD 2023 (Learning from Temporal Data) is an academic conference held in Thessaloniki, Greece on 2023-10-09. The paper submission deadline is 2023-05-22. Acceptance notifications are sent on 2023-07-17.

The scope of the special session includes but is not limited to the following: Temporal data clustering Classification and regression of univariate and multivariate time series Early classification of temporal data Deep learning for temporal data Learning representation for temporal data Metric and kernel learning for temporal data Modeling temporal dependencies Time series forecasting Time series annotation, segmentation, and anomaly detection Spatial-temporal statistical analysis Functional data analysis methods Data streams Interpretable/explainable time-series analysis methods Dimensionality reduction, sparsity, algorithmic complexity, and big data challenges Benchmarking and assessment methods for temporal data Applications, including transport, urban computing, weather and climate, ecology, bio-informatics, medical, and energy consumption on temporal data
Last updated by Dou Sun on

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