Time-dependent sequential data emerge in many key real-world problems, including areas such as climate, robotics, economics, entertainment, healthcare and transportation. The increasing volume and complexity of time series data in modern applications highlight the importance of scalable and flexible time series learning techniques. Predominant methods in machine learning often assume i.i.d. observations, which is generally not appropriate for time series data. Therefore, there is both a great need and an exciting opportunity for the machine learning community to develop theory, models, and algorithms for processing and analyzing large-scale complex time series data. This Workshop will bring awareness among researchers & academicians at the forefront of time series analysis and algorithms to discuss existing key progress and promising new directions.