Submissions
Papers must be 4 to 6 pages in length (excluding references and appendices), follow the ACML LaTeX template, and be anonymised for blind review.
Machine Learning/CORE Unranked
FMLTS 2026
Future of Machine Learning for Time Series
FMLTS 2026 is a workshop held in conjunction with ACML 2026 that brings together researchers and practitioners to explore future directions in machine learning for time series. It addresses pressing challenges such as benchmarking weaknesses, small datasets, and the shift from traditional methods to deep learning and foundation models.
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Research areas in scope
01
Topics
time series classificationforecastinganomaly detectionextrinsic regressiondatasets, benchmarking, and generalisationbias and varianceinductive bias and 'no free lunch'deep learning and foundation modelsembeddings and representationsscalabilitysegmentation
Policies worth checking twice
- Submissions must be anonymised.
- Submissions must be 4 to 6 pages in length (excluding references and appendices).
- Submissions must be made via OpenReview.
- Each submission will be reviewed by three reviewers.
- OpenReview profile creation with non-institutional emails may take up to two weeks.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026