Currently, most real-world time series datasets are multivariate and are rich in dynamical
information of the underlying system. Such datasets are attracting much attention; therefore …

Time series classification is an important research topic in machine learning and data
mining communities, since time series data exist in many application domains. Recent …

Recently, as real-time sensor data collection increases in various fields such as power
plants, smart factories, and health care systems, anomaly detection for multivariate time …

Spatio-temporal problems arise in a broad range of applications, such as climate science
and transportation systems. These problems are challenging because of unique spatial …

Time-series forecasting is an important problem across a wide range of domains. Designing
accurate and prompt forecasting algorithms is a non-trivial task, as temporal data that arise …

The systemic nature of the risk of bankruptcy of financial institutions has become an
important issue in maintaining the existence and stability of domestic and global finance …

Time series data classification is a significant topic as its application can be found in a
various domain. Recent studies have shown that data-driven approach based on deep …

The harsh environmental conditions in a marine area make continuous observations of it
challenging. To temporally or permanently replace faulty hardware sensors, reliable virtual …

Given sensor readings over time from a power grid, how can we accurately detect when an
anomaly occurs? A key part of achieving this goal is to use the network of power grid …

This paper provides an approach to the detection of information security breaches in
automated process control systems (APCS), which consists in forecasting multivariate time …