AMLTS22: Applied Machine Learning Methods for Time Series Forecasting Atlanta Georgia, GA, United States, October 24, 2022 |
Conference website | https://amlts.github.io/amlts2022/ |
Submission link | https://easychair.org/conferences/?conf=amlts22 |
Abstract registration deadline | August 15, 2022 |
Submission deadline | August 15, 2022 |
Applied Machine Learning Methods for Time Series Forecasting (AMLTS): https://amlts.github.io/amlts2022/
Workshop held in conjunction with CIKM 2022: https://www.cikm2022.org/
Time series data is ubiquitous, and accurate time series forecasting is vital for many real-world application domains, including retail, healthcare, supply chain, climate science, e-commerce and economics. The choice of machine learning methods, both conventional and deep learning-based models, primarily depends on the nature of input data. In addition, several models have been adopted in industries with great success.
We invite quality, novel, and ingenious contributions within an industrial application setting. The papers may span across and are not limited to achievements addressing relevant forecasting challenges in retail, e-commerce, and online transaction systems. We invite submissions of long and short papers of two to eight pages (including references), representing actual industrial deployment, preliminary results, and proposals for new work in industry or academics. All submissions will be single-blind and peer-reviewed by an international program committee of researchers/industrial professionals with a high reputation. Accepted submissions will be required to be presented at the workshop.
Submission Guidelines
Paper Submission Deadline: August 15, 2022, 11:59 PM AoE.
This workshop follows the submission requirement by CIKM.
Rules:
- Long paper (up to 8 pages) and short paper (up to 4 pages). The page limit includes the bibliography and any possible appendices.
- All papers must be formatted according to ACM sigconf template manuscript style, following the submission guidelines available at: https://www.acm.org/publications/proceedings-template.
- Papers should be submitted in PDF format, electronically, using the EasyChair submission system.
- All selected papers will invited for presentation.
List of Topics
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Topics of interest on applied machine-learning time-series forecasting approaches include, but are not limited to:
Model/ Architecture-based:
- Effective classical and deep learning-based forecasting
- Probabilistic/Statistical forecasting models
- Novel/Enhanced approaches for short-term/long-term forecasting
- Change point detection/ extreme event forecasting models
- Outlier detection/removal in forecasting
- Bayesian models/ neural network models to quantify forecasting uncertainty.
Model Evaluation/Metrics:
- Quantifying the performance of the proposed method for forecasting output (causal inference, statistics methods.)
- Online/offline/ real-time based training/prediction models
- Existing/Improved evaluation metrics and performance study
Large-Scale Deployment:
- Scalable, automated forecasting pipeline applications.
- Best practices for sampling to solve scalability issues in forecasting
- Challenges and resolutions to scale forecasting models to big data.
Committees
Organizing committee
- Linsey Pang: Salesforce
- Wei Liu: University of Technology Sydney
- LingFei Wu: JD.COM
- Kexin Xie: Salesforce
- Stephen Guo: Walmart Global Tech
- Raghav Chalapathy: Walmart Global Tech
- Musen Wen: Walmart Global Tech
Contact
All questions about submissions should be emailed to amlts22@googlegroups.com