The 12th Mining and Learning from Time Series (MILETS)

Time series data are ubiquitous. In domains as diverse as finance, retail, entertainment, transportation and health care, we observe a fundamental shift away from parsimonious, infrequent measurement to nearly continuous monitoring and recording. Recent advances in diverse sensing technologies, ranging from remote sensors to wearables and social sensing, are generating a rapid growth in the size and complexity of time series archives. Thus, although time series analysis has been studied extensively, its importance only continues to grow. What is more, modern time series data pose significant challenges to existing techniques (e.g., irregular sampling in hospital records and spatiotemporal structure in climate data). Finally, time series mining research is challenging and rewarding because it bridges a variety of disciplines and demands interdisciplinary solutions. Now is the time to discuss the next generation of temporal mining algorithms.

The focus of MILETS is to synergize the research in this area and discuss both new and open problems in time series analysis and mining. The solutions to these problems may be algorithmic, theoretical, statistical, or systems-based in nature. Further, MILETS emphasizes applications to high impact or relatively new domains, including but not limited to biology, health and medicine, climate and weather, road traffic, astronomy, and energy.
The workshop will discuss a broad variety of topics related to time series, including:

• Time series forecasting and prediction using classical approaches.
• Time series forecasting and prediction using LLMs.
• Time series pattern mining and detection, representation, searching and indexing, classification, clustering, prediction, forecasting, and rule mining.
• Time series with special structure: spatiotemporal, relational, hierarchical, and other complex forms.
• Time series with sparse or irregular sampling, missing values, and special types of measurement noise or bias.
• Time series that are multivariate, high-dimensional, heterogeneous, or that possess other atypical properties.
• Time series analysis using less traditional approaches, such as deep learning and subspace clustering.
• Privacy-preserving time series mining and learning.
• Online, high-speed learning and mining from streaming time series.
• Uncertain time series mining.
• Applications to high impact or relatively new time series domains, such as health and medicine, road traffic, and air quality.
• New, open, or unsolved problems in time series analysis and mining.

Submissions should follow the SIGKDD formatting requirements (unless otherwise stated) and will be evaluated using the SIGKDD Research Track evaluation criteria. Preference will be given to papers that are reproducible, and authors are encouraged to share their data and code publicly whenever possible. Detailed submission instructions for MILETS 2026 will be announced soon.

Note on open problem submissions: In order to promote new and innovative research on time series, we plan to accept a small number of high quality manuscripts describing open problems in time series analysis and mining. Such papers should provide a clear, detailed description and analysis of a new or open problem that poses a significant challenge to existing techniques, as well as a thorough empirical investigation demonstrating that current methods are insufficient.

The review process is expected to be single-round and double-blind. Accepted papers will be presented during the workshop and listed on the website. Additional presentation details will be announced later.

Any questions may be directed to the workshop e-mail address: kdd.milets@gmail.com.

Call for Papers here.