Time series data are becoming ubiquitous in numerous real world applications, e.g., IoT devices, healthcare, wearable devices, smart vehicles, financial markets, biological sciences, environmental sciences, etc. Given the availability of massive amounts of data, their complex underlying structures/distributions, together with the high-performance computing platforms, there is a great demand for developing new theories and algorithms to tackle some fundamental challenges (e.g., representation, classification, prediction, causal analysis, etc.) in various types of applications


    The goal of this workshop is to provide a platform for researchers and AI practitioners from both academia and industry to discuss potential research directions, key technical issues, and present solutions to tackle related issues in practical applications. The workshop will focus on both the theoretical and practical aspects of time series data analysis and aims to trigger research innovations on theories, algorithms, and applications. We will invite researchers and AI practitioners from the related areas of machine learning, data science, statistics, econometrics, and many others to contribute to this workshop.


    This workshop encourages submissions of innovative solutions for a broad range of time series analysis problems. Topics of interest include but are not limited to the following:

• Time series prediction
• Classification and clustering
• Anomaly detection and diagnosis/change point detection
• Similarity search
• Time series indexing
• Pattern discovery
• Interpretation and explanation
• Bias and fairness
• Causal inference
• Spatio-temporal prediction
• Predictive maintenance
• Healthcare
• Fintech
• Traffic analysis
• Weather forecasting
• Internet of things
• Machine learning
• Data Mining
• Artificial Intelligence
• AI-inspired approaches for time series similarity search
• Experimental evaluation and comparison of AI and traditional techniques
• New benchmarks for time series analysis tasks